Mercurial > repos > cpt > cpt_helical_wheel
changeset 1:9b276485c94a draft
planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
| author | cpt |
|---|---|
| date | Mon, 05 Jun 2023 02:44:43 +0000 |
| parents | 9caa9aa44fd8 |
| children | e9fb56f44c35 |
| files | cpt-macros.xml cpt_helical_wheel/cpt-macros.xml cpt_helical_wheel/generateHelicalWheel.py cpt_helical_wheel/generateHelicalWheel.xml cpt_helical_wheel/macros.xml cpt_helical_wheel/plotWheels/__init__.py cpt_helical_wheel/plotWheels/core.py cpt_helical_wheel/plotWheels/descriptors.py cpt_helical_wheel/plotWheels/helical_wheel.py generateHelicalWheel.py generateHelicalWheel.xml macros.xml plotWheels/__init__.py plotWheels/core.py plotWheels/descriptors.py plotWheels/helical_wheel.py |
| diffstat | 14 files changed, 5680 insertions(+), 3198 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cpt-macros.xml Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,115 @@ +<macros> + <xml name="gff_requirements"> + <requirements> + <requirement type="package" version="2.7">python</requirement> + <requirement type="package" version="1.65">biopython</requirement> + <requirement type="package" version="2.12.1">requests</requirement> + <requirement type="package" version="1.2.2">cpt_gffparser</requirement> + <yield/> + </requirements> + <version_command> + <![CDATA[ + cd '$__tool_directory__' && git rev-parse HEAD + ]]> + </version_command> + </xml> + <xml name="citation/mijalisrasche"> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex">@unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + </xml> + <xml name="citations"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-crr"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Ross}, + title = {CPT Galaxy Tools}, + year = {2020-}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-2020"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {E. Mijalis, H. Rasche}, + title = {CPT Galaxy Tools}, + year = {2013-2017}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {A. Criscione}, + title = {CPT Galaxy Tools}, + year = {2019-2021}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-2020-AJC-solo"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {A. Criscione}, + title = {CPT Galaxy Tools}, + year = {2019-2021}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="citations-clm"> + <citations> + <citation type="doi">10.1371/journal.pcbi.1008214</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Maughmer}, + title = {CPT Galaxy Tools}, + year = {2017-2020}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </citations> + </xml> + <xml name="sl-citations-clm"> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Maughmer}, + title = {CPT Galaxy Tools}, + year = {2017-2020}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + <yield/> + </xml> +</macros>
--- a/cpt_helical_wheel/cpt-macros.xml Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,115 +0,0 @@ -<?xml version="1.0"?> -<macros> - <xml name="gff_requirements"> - <requirements> - <requirement type="package" version="2.7">python</requirement> - <requirement type="package" version="1.65">biopython</requirement> - <requirement type="package" version="2.12.1">requests</requirement> - <yield/> - </requirements> - <version_command> - <![CDATA[ - cd $__tool_directory__ && git rev-parse HEAD - ]]> - </version_command> - </xml> - <xml name="citation/mijalisrasche"> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex">@unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - </xml> - <xml name="citations"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-crr"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Ross}, - title = {CPT Galaxy Tools}, - year = {2020-}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-2020"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {E. Mijalis, H. Rasche}, - title = {CPT Galaxy Tools}, - year = {2013-2017}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {A. Criscione}, - title = {CPT Galaxy Tools}, - year = {2019-2021}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-2020-AJC-solo"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {A. Criscione}, - title = {CPT Galaxy Tools}, - year = {2019-2021}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="citations-clm"> - <citations> - <citation type="doi">10.1371/journal.pcbi.1008214</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Maughmer}, - title = {CPT Galaxy Tools}, - year = {2017-2020}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </citations> - </xml> - <xml name="sl-citations-clm"> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Maughmer}, - title = {CPT Galaxy Tools}, - year = {2017-2020}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - <yield/> - </xml> -</macros>
--- a/cpt_helical_wheel/generateHelicalWheel.py Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,86 +0,0 @@ -## - -import argparse -from plotWheels.helical_wheel import helical_wheel - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Generate Helical Wheel") - parser.add_argument("--sequence",dest="sequence",type=str) - parser.add_argument("--seqRange",dest="seqRange",type=int,default=1) - parser.add_argument("--t_size",dest="t_size",type=int,default=32) - parser.add_argument("--rotation",dest="rotation",type=int,default=90) - parser.add_argument("--numbering",action="store_true",help="numbering for helical wheel") - parser.add_argument("--output",dest="output",type=argparse.FileType("wb"), default="_helicalwheel.png")#dest="output",default="_helicalwheel.png") - #### circle colors - parser.add_argument("--f_A",dest="f_A", default="#ffcc33") - parser.add_argument("--f_C",dest="f_C",default="#b5b5b5") - parser.add_argument("--f_D",dest="f_D",default="#db270f") - parser.add_argument("--f_E",dest="f_E",default="#db270f") - parser.add_argument("--f_F",dest="f_F",default="#ffcc33") - parser.add_argument("--f_G",dest="f_G",default="#b5b5b5") - parser.add_argument("--f_H",dest="f_H",default="#12d5fc") - parser.add_argument("--f_I",dest="f_I",default="#ffcc33") - parser.add_argument("--f_K",dest="f_K",default="#12d5fc") - parser.add_argument("--f_L",dest="f_L",default="#ffcc33") - parser.add_argument("--f_M",dest="f_M",default="#ffcc33") - parser.add_argument("--f_N",dest="f_N",default="#b5b5b5") - parser.add_argument("--f_P",dest="f_P",default="#ffcc33") - parser.add_argument("--f_Q",dest="f_Q",default="#b5b5b5") - parser.add_argument("--f_R",dest="f_R",default="#12d5fc") - parser.add_argument("--f_S",dest="f_S",default="#b5b5b5") - parser.add_argument("--f_T",dest="f_T",default="#b5b5b5") - parser.add_argument("--f_V",dest="f_V",default="#ffcc33") - parser.add_argument("--f_W",dest="f_W",default="#ffcc33") - parser.add_argument("--f_Y",dest="f_Y",default="#b5b5b5") - ### text colors - parser.add_argument("--t_A",dest="t_A",default="k") - parser.add_argument("--t_C",dest="t_C",default="k") - parser.add_argument("--t_D",dest="t_D",default="w") - parser.add_argument("--t_E",dest="t_E",default="w") - parser.add_argument("--t_F",dest="t_F",default="k") - parser.add_argument("--t_G",dest="t_G",default="k") - parser.add_argument("--t_H",dest="t_H",default="k") - parser.add_argument("--t_I",dest="t_I",default="k") - parser.add_argument("--t_K",dest="t_K",default="k") - parser.add_argument("--t_L",dest="t_L",default="k") - parser.add_argument("--t_M",dest="t_M",default="k") - parser.add_argument("--t_N",dest="t_N",default="k") - parser.add_argument("--t_P",dest="t_P",default="k") - parser.add_argument("--t_Q",dest="t_Q",default="k") - parser.add_argument("--t_R",dest="t_R",default="k") - parser.add_argument("--t_S",dest="t_S",default="k") - parser.add_argument("--t_T",dest="t_T",default="k") - parser.add_argument("--t_V",dest="t_V",default="k") - parser.add_argument("--t_W",dest="t_W",default="k") - parser.add_argument("--t_Y",dest="t_Y",default="k") - - args = parser.parse_args() - - - #print(type(args.output)) - - f_colors = [args.f_A,args.f_C,args.f_D,args.f_E,args.f_F,args.f_G,args.f_H,args.f_I,args.f_K, - args.f_L,args.f_M,args.f_N,args.f_P,args.f_Q,args.f_R,args.f_S,args.f_T,args.f_V, - args.f_W,args.f_Y] - - t_colors = [args.t_A,args.t_C,args.t_D,args.t_E,args.t_F,args.t_G,args.t_H,args.t_I,args.t_K, - args.t_L,args.t_M,args.t_N,args.t_P,args.t_Q,args.t_R,args.t_S,args.t_T,args.t_V, - args.t_W,args.t_Y] - - colors = [f_colors, t_colors] - - tmp_file = "./tmp.png" - - helical_wheel(sequence=args.sequence, - colorcoding=colors[0], - text_color=colors[1], - seqRange=args.seqRange, - t_size=args.t_size, - rot=args.rotation, - numbering=args.numbering, - filename=tmp_file - ) - - with open("tmp.png", "rb") as f: - for line in f: - args.output.write(line)
--- a/cpt_helical_wheel/generateHelicalWheel.xml Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,388 +0,0 @@ -<?xml version="1.1"?> -<tool id="edu.tamu.cpt2.helicalWheel.generateHelicalWheel" name="Helical Wheel" version="1.0"> - <description>Generate and Plot a Protein Helical Wheel</description> - <macros> - <import>cpt-macros.xml</import> - <import>macros.xml</import> - </macros> - <expand macro="requirements"> - <requirement type="package">numpy</requirement> - <requirement type="package">pandas</requirement> - <requirement type="package" version="0.18.1">scikit-learn</requirement> - <requirement type="package">scipy</requirement> - <requirement type="package">matplotlib</requirement> - </expand> - <command detect_errors="aggressive"><![CDATA[ -python $__tool_directory__/generateHelicalWheel.py ---sequence $sequence ---seqRange $seqRange ---t_size $t_size ---rotation $rotation -$numbering ---f_A "$sec_B.f_A" ---f_C "$sec_C.f_C" ---f_D "$sec_D.f_D" ---f_E "$sec_D.f_E" ---f_F "$sec_B.f_F" ---f_G "$sec_C.f_G" ---f_H "$sec_E.f_H" ---f_I "$sec_B.f_I" ---f_K "$sec_E.f_K" ---f_L "$sec_B.f_L" ---f_M "$sec_B.f_M" ---f_N "$sec_C.f_N" ---f_P "$sec_B.f_P" ---f_Q "$sec_C.f_Q" ---f_R "$sec_E.f_R" ---f_S "$sec_C.f_S" ---f_T "$sec_C.f_T" ---f_V "$sec_B.f_V" ---f_W "$sec_B.f_W" ---f_Y "$sec_C.f_Y" ---t_A "$sec_B.t_A" ---t_C "$sec_C.t_C" ---t_D "$sec_D.t_D" ---t_E "$sec_D.t_E" ---t_F "$sec_B.t_F" ---t_G "$sec_C.t_G" ---t_H "$sec_E.t_H" ---t_I "$sec_B.t_I" ---t_K "$sec_E.t_K" ---t_L "$sec_B.t_L" ---t_M "$sec_B.t_M" ---t_N "$sec_C.t_N" ---t_P "$sec_B.t_P" ---t_Q "$sec_C.t_Q" ---t_R "$sec_E.t_R" ---t_S "$sec_C.t_S" ---t_T "$sec_C.t_T" ---t_V "$sec_B.t_V" ---t_W "$sec_B.t_W" ---t_Y "$sec_C.t_Y" ---output $output -]]></command> - <inputs> - <param label="Paste in exact sequence to be plotted" name="sequence" type="text" /> - <param label="Label Start Number" name="seqRange" type="integer" value="1" help="starting residue number to use for labels" /> - <param label="Amino Acid Text Size" name="t_size" type="integer" value="32" help="Alters the Text Size. Default is 32" /> - <param label="Rotation" name="rotation" type="integer" value="90" help="Rotates the helical wheel. Default is 90" /> - <param label="Label Numbering Text" name="numbering" type="boolean" help="number schema subscripts" truevalue="--numbering" falsevalue=""/> - <section name="sec_B" title="nonpolar ; hydrophobic"> - <param name="f_A" type="color" label="Color for A" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_A" type="color" label="Text color for A" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_F" type="color" label="Color for F" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_F" type="color" label="Text color for F" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_I" type="color" label="Color for I" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_I" type="color" label="Text color for I" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_L" type="color" label="Color for L" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_L" type="color" label="Text color for L" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_M" type="color" label="Color for M" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_M" type="color" label="Text color for M" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_P" type="color" label="Color for P" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_P" type="color" label="Text color for P" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_V" type="color" label="Color for V" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_V" type="color" label="Text color for V" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_W" type="color" label="Color for W" value="#ffcc33"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_W" type="color" label="Text color for W" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - </section> - <section name="sec_C" title="polar ; uncharged"> - <param name="f_C" type="color" label="Color for C" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_C" type="color" label="Text color for C" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_G" type="color" label="Color for G" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_G" type="color" label="Text color for G" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_N" type="color" label="Color for N" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_N" type="color" label="Text color for N" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_Q" type="color" label="Color for Q" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_Q" type="color" label="Text color for Q" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_S" type="color" label="Color for S" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_S" type="color" label="Text color for S" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_T" type="color" label="Color for T" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_T" type="color" label="Text color for T" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_Y" type="color" label="Color for Y" value="#b5b5b5"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_Y" type="color" label="Text color for Y" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - </section> - <section name="sec_D" title="polar ; acidic (negatively charged)"> - <param name="f_D" type="color" label="Color for D" value="#db270f"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_D" type="color" label="Text color for D" value="#FFFFFF"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_E" type="color" label="Color for E" value="#db270f"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_E" type="color" label="Text color for E" value="#FFFFFF"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - </section> - <section name="sec_E" title="polar ; basic (positive charge)"> - <param name="f_H" type="color" label="Color for H" value="#12d5fc"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_H" type="color" label="Text color for H" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_K" type="color" label="Color for K" value="#12d5fc"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_K" type="color" label="Text color for K" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="f_R" type="color" label="Color for R" value="#12d5fc"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - <param name="t_R" type="color" label="Text color for R" value="#000000"> - <sanitizer> - <valid initial="string.ascii_letters,string.digits"> - <add value="#" /> - </valid> - </sanitizer> - </param> - </section> - </inputs> - <outputs> - <data format="png" name="output" label="_helicalWheel.png" /> - </outputs> - <help><![CDATA[ -**What it does** -INPUT : Peptide Sequence -PARAMETERS : -primary parameters : -> Paste in exact sequence to be plotted - Input Sequence of desired helical wheel plot -> Label Start Number - Numerical value that represents the beginning of the sequence (default 1) -> Amino Acid Text Size - Size of text for helical wheel (default 32) -> Rotation - Degrees to rotate helical wheel (defaul 90) -color parameters : -> Background Color and Text Color Selections -METHOD : Using the core features from the modlAMP python module, a helical wheel projection is constructed. -OUTPUT : _helicalWheel.png -NOTES : Peptide lengths longer than 36 residues will not properly graph. -]]></help> - <citations> - <citation type="doi">10.1093/bioinformatics/btx285</citation> - <citation type="bibtex"> - @unpublished{galaxyTools, - author = {C. Ross}, - title = {CPT Galaxy Tools}, - year = {2020-}, - note = {https://github.com/tamu-cpt/galaxy-tools/} - } - </citation> - </citations> -</tool>
--- a/cpt_helical_wheel/macros.xml Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,56 +0,0 @@ -<?xml version="1.0"?> -<macros> - <xml name="requirements"> - <requirements> - <requirement type="package" version="3.6">python</requirement> - <requirement type="package" version="1.77">biopython</requirement> - <requirement type="package" version="1.1.3">cpt_gffparser</requirement> - <yield/> - </requirements> - </xml> - <xml name="genome_selector"> - <conditional name="reference_genome"> - <param name="reference_genome_source" type="select" label="Reference Genome"> - <option value="history" selected="True">From History</option> - <option value="cached">Locally Cached</option> - </param> - <when value="cached"> - <param name="fasta_indexes" type="select" label="Source FASTA Sequence"> - <options from_data_table="all_fasta"/> - </param> - </when> - <when value="history"> - <param name="genome_fasta" type="data" format="fasta" label="Source FASTA Sequence"/> - </when> - </conditional> - </xml> - <xml name="gff3_input"> - <param label="GFF3 Annotations" name="gff3_data" type="data" format="gff3"/> - </xml> - <xml name="input/gff3+fasta"> - <expand macro="gff3_input" /> - <expand macro="genome_selector" /> - </xml> - <token name="@INPUT_GFF@"> - "$gff3_data" - </token> - <token name="@INPUT_FASTA@"> -#if str($reference_genome.reference_genome_source) == 'cached': - "${reference_genome.fasta_indexes.fields.path}" -#else if str($reference_genome.reference_genome_source) == 'history': - genomeref.fa -#end if - </token> - <token name="@GENOME_SELECTOR_PRE@"> -#if $reference_genome.reference_genome_source == 'history': - ln -s $reference_genome.genome_fasta genomeref.fa; -#end if - </token> - <token name="@GENOME_SELECTOR@"> -#if str($reference_genome.reference_genome_source) == 'cached': - "${reference_genome.fasta_indexes.fields.path}" -#else if str($reference_genome.reference_genome_source) == 'history': - genomeref.fa -#end if - </token> -</macros>
--- a/cpt_helical_wheel/plotWheels/core.py Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1223 +0,0 @@ -# -*- coding: utf-8 -*- -""" -.. currentmodule:: modlamp.core - -.. moduleauthor:: modlab Alex Mueller ETH Zurich <alex.mueller@pharma.ethz.ch> - -Core helper functions and classes for other modules. The two main classes are: - -============================= ======================================================================================= -Class Characteristics -============================= ======================================================================================= -:py:class:`BaseSequence` Base class inheriting to all sequence classes in the module :py:mod:`modlamp.sequences` -:py:class:`BaseDescriptor` Base class inheriting to the two descriptor classes in :py:mod:`modlamp.descriptors` -============================= ======================================================================================= -""" - -import os -import random -import re - -import numpy as np -import pandas as pd -import collections -import operator -from scipy.spatial import distance -from sklearn.preprocessing import MinMaxScaler, StandardScaler -from sklearn.utils import shuffle - -__author__ = "Alex Müller, Gisela Gabernet" -__docformat__ = "restructuredtext en" - - -class BaseSequence(object): - """Base class for sequence classes in the module :mod:`modlamp.sequences`. - It contains amino acid probabilities for different sequence generation classes. - - The following amino acid probabilities are used: (extracted from the - `APD3 <http://aps.unmc.edu/AP/statistic/statistic.php>`_, March 17, 2016) - - === ==== ====== ========= ========== - AA rand AMP AMPnoCM randnoCM - === ==== ====== ========= ========== - A 0.05 0.0766 0.0812275 0.05555555 - C 0.05 0.071 0.0 0.0 - D 0.05 0.026 0.0306275 0.05555555 - E 0.05 0.0264 0.0310275 0.05555555 - F 0.05 0.0405 0.0451275 0.05555555 - G 0.05 0.1172 0.1218275 0.05555555 - H 0.05 0.021 0.0256275 0.05555555 - I 0.05 0.061 0.0656275 0.05555555 - K 0.05 0.0958 0.1004275 0.05555555 - L 0.05 0.0838 0.0884275 0.05555555 - M 0.05 0.0123 0.0 0.0 - N 0.05 0.0386 0.0432275 0.05555555 - P 0.05 0.0463 0.0509275 0.05555555 - Q 0.05 0.0251 0.0297275 0.05555555 - R 0.05 0.0545 0.0591275 0.05555555 - S 0.05 0.0613 0.0659275 0.05555555 - T 0.05 0.0455 0.0501275 0.05555555 - V 0.05 0.0572 0.0618275 0.05555555 - W 0.05 0.0155 0.0201275 0.05555555 - Y 0.05 0.0244 0.0290275 0.05555555 - === ==== ====== ========= ========== - - """ - - def __init__(self, seqnum, lenmin=7, lenmax=28): - """ - :param seqnum: number of sequences to generate - :param lenmin: minimal length of the generated sequences - :param lenmax: maximal length of the generated sequences - :return: attributes :py:attr:`seqnum`, :py:attr:`lenmin` and :py:attr:`lenmax`. - :Example: - - >>> b = BaseSequence(10, 7, 28) - >>> b.seqnum - 10 - >>> b.lenmin - 7 - >>> b.lenmax - 28 - """ - self.sequences = list() - self.names = list() - self.lenmin = int(lenmin) - self.lenmax = int(lenmax) - self.seqnum = int(seqnum) - - # AA classes: - self.AA_hyd = ['G', 'A', 'L', 'I', 'V'] - self.AA_basic = ['K', 'R'] - self.AA_acidic = ['D', 'E'] - self.AA_aroma = ['W', 'Y', 'F'] - self.AA_polar = ['S', 'T', 'Q', 'N'] - # AA labels: - self.AAs = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] - # AA probability from the APD3 database: - self.prob_AMP = [0.0766, 0.071, 0.026, 0.0264, 0.0405, 0.1172, 0.021, 0.061, 0.0958, 0.0838, 0.0123, 0.0386, - 0.0463, 0.0251, 0.0545, 0.0613, 0.0455, 0.0572, 0.0155, 0.0244] - # AA probability from the APD2 database without Cys and Met (synthesis reasons) - self.prob_AMPnoCM = [0.081228, 0., 0.030627, 0.031027, 0.045128, 0.121828, 0.025627, 0.065628, 0.100428, - 0.088428, 0., 0.043228, 0.050928, 0.029728, 0.059128, 0.065927, 0.050128, 0.061828, - 0.020128, 0.029028] - # equal AA probabilities: - self.prob = [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, - 0.05, 0.05, 0.05, 0.05] - # equal AA probabilities but 0 for Cys and Met: - self.prob_randnoCM = [0.05555555555, 0.0, 0.05555555555, 0.05555555555, 0.05555555555, 0.05555555555, - 0.05555555555, 0.05555555555, 0.05555555555, 0.05555555555, 0.0, 0.05555555555, - 0.05555555555, 0.05555555555, 0.05555555555, 0.05555555555, 0.05555555555, 0.05555555555, - 0.05555555555, 0.05555555555] - - # AA probability from the linear CancerPPD peptides: - self.prob_ACP = [0.14526966, 0., 0.00690031, 0.00780824, 0.06991102, 0.04957327, 0.01725077, 0.05647358, - 0.27637552, 0.17759216, 0.00998729, 0.00798983, 0.01307427, 0.00381333, 0.02941711, - 0.02651171, 0.0154349, 0.04013074, 0.0406755, 0.00581079] - - # AA probabilities for perfect amphipathic helix of different arc sizes - self.prob_amphihel = [[0.04545455, 0., 0.04545454, 0.04545455, 0., 0.04545455, 0.04545455, 0., 0.25, 0., 0., - 0.04545454, 0.04545455, 0.04545454, 0.25, 0.04545454, 0.04545454, 0., 0., 0.04545454], - [0., 0., 0., 0., 0.16666667, 0., 0., 0.16666667, 0., 0.16666667, 0., 0., 0., 0., 0., 0., - 0., 0.16666667, 0.16666667, (1. - 0.16666667 * 5)]] - - # helical ACP AA probabilities, depending on the position of the AA in the helix. - self.prob_ACPhel = np.array([[0.0483871, 0., 0., 0.0483871, 0.01612903, 0.12903226, 0.03225807, 0.09677419, - 0.19354839, 0.5, 0.0483871, 0.11290323, 0.1, 0.18518519, 0.07843137, 0.12, - 0.17073172, 0.16666667], - [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.01612903, 0., 0., 0., 0., 0., - 0.02439024, - 0.19444444], - [0., 0.01612903, 0., 0.27419355, 0.01612903, 0., 0., 0.01612903, 0., 0., 0., 0., - 0., - 0., 0., 0., 0., 0.], - [0., 0., 0., 0., 0., 0., 0., 0.06451613, 0., 0.01612903, 0.0483871, 0.01612903, 0., - 0.01851852, 0., 0., 0., 0.], - [0.16129032, 0.0483871, 0.30645161, 0., 0.0483871, 0., 0., 0.01612903, 0., - 0.01612903, - 0., 0.09677419, 0.06666667, 0.01851852, 0., 0.02, 0.14634146, 0.], - [0.64516129, 0., 0.17741936, 0.14516129, 0., 0.01612903, 0.25806452, 0.11290323, - 0.06451613, 0.08064516, 0.22580645, 0.03225807, 0.06666667, 0.2037037, 0.1372549, - 0.1, 0., 0.05555556], - [0., 0., 0., 0.01612903, 0., 0., 0.01612903, 0., 0.03225807, 0., 0., 0.20967742, - 0., - 0., 0., 0.16, 0., 0.], - [0.0483871, 0.11290323, 0.01612903, 0.08064516, 0.33870968, 0.27419355, 0., - 0.0483871, 0.14516129, 0.06451613, 0.03225807, 0.06451613, 0.18333333, 0., 0., - 0.1, 0.26829268, 0.], - [0., 0.03225807, 0.01612903, 0.12903226, 0.12903226, 0., 0.38709677, 0.33870968, - 0.0483871, 0.03225807, 0.41935484, 0.08064516, 0., 0.03703704, 0.29411765, - 0.04, 0.02439024, 0.02777778], - [0.0483871, 0.70967742, 0.12903226, 0.0483871, 0.09677419, 0.32258064, 0.20967742, - 0.06451613, 0.11290323, 0.06451613, 0.03225807, 0.03225807, 0.28333333, - 0.24074074, - 0.03921569, 0.28, 0.07317073, 0.22222222], - [0., 0.01612903, 0.01612903, 0.0483871, 0.01612903, 0.03225807, 0., 0., 0., 0., - 0., 0., 0.03333333, 0., 0.01960784, 0.02, 0., 0.], - [0., 0.01612903, 0., 0., 0., 0., 0., 0., 0.01612903, 0., 0.03225807, 0., 0., 0., - 0.01960784, 0.02, 0., 0.], - [0., 0., 0.14516129, 0.01612903, 0.03225807, 0.01612903, 0., 0., 0., 0., - 0.01612903, 0., 0., 0.12962963, 0.17647059, 0., 0., 0.], - [0., 0., 0.01612903, 0.01612903, 0., 0., 0.01612903, 0., 0.01612903, 0., 0., - 0.01612903, 0., 0.01851852, 0., 0., 0., 0.], - [0., 0.01612903, 0.01612903, 0., 0.01612903, 0., 0.01612903, 0., 0.01612903, - 0.01612903, 0.01612903, 0.01612903, 0., 0.01851852, 0.01960784, 0., 0.04878049, - 0.], - [0.01612903, 0., 0.01612903, 0.12903226, 0.03225807, 0.03225807, 0.0483871, - 0.17741936, 0., 0.03225807, 0.09677419, 0.0483871, 0.01666667, 0., 0.15686274, - 0.1, 0., 0.05555556], - [0.01612903, 0.01612903, 0., 0.01612903, 0.0483871, 0.01612903, 0., 0.01612903, 0., - 0.01612903, 0.01612903, 0.11290323, 0., 0.01851852, 0.03921569, 0.02, 0., - 0.05555556], - [0.01612903, 0.01612903, 0.01612903, 0.01612903, 0.20967742, 0.16129032, - 0.01612903, - 0.0483871, 0.33870968, 0.16129032, 0., 0.14516129, 0.25, 0.11111111, 0.01960784, - 0.02, 0.21951219, 0.22222222], - [0., 0., 0.12903226, 0.01612903, 0., 0., 0., 0., 0.01612903, 0., 0., 0., 0., 0., - 0., - 0., 0.02439024, 0.], - [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.01612903, 0., 0., 0., 0., 0., 0.]]) - - def save_fasta(self, filename, names=False): - """Method to save generated sequences in a ``.FASTA`` formatted file. - - :param filename: output filename in which the sequences from :py:attr:`sequences` are safed in fasta format. - :param names: {bool} whether sequence names from :py:attr:`names` should be saved as sequence identifiers - :return: a FASTA formatted file containing the generated sequences - :Example: - - >>> b = BaseSequence(2) - >>> b.sequences = ['KLLSLSLALDLLS', 'KLPERTVVNSSDF'] - >>> b.names = ['Sequence1', 'Sequence2'] - >>> b.save_fasta('/location/of/fasta/file.fasta', names=True) - """ - if names: - save_fasta(filename, self.sequences, self.names) - else: - save_fasta(filename, self.sequences) - - def mutate_AA(self, nr, prob): - """Method to mutate with **prob** probability a **nr** of positions per sequence randomly. - - :param nr: number of mutations to perform per sequence - :param prob: probability of mutating a sequence - :return: mutated sequences in the attribute :py:attr:`sequences`. - :Example: - - >>> b = BaseSequence(1) - >>> b.sequences = ['IAKAGRAIIK'] - >>> b.mutate_AA(3, 1.) - >>> b.sequences - ['NAKAGRAWIK'] - """ - for s in range(len(self.sequences)): - # mutate: yes or no? prob = mutation probability - mutate = np.random.choice([1, 0], 1, p=[prob, 1 - float(prob)]) - if mutate == 1: - seq = list(self.sequences[s]) - cnt = 0 - while cnt < nr: # mutate "nr" AA - seq[random.choice(range(len(seq)))] = random.choice(self.AAs) - cnt += 1 - self.sequences[s] = ''.join(seq) - - def filter_duplicates(self): - """Method to filter duplicates in the sequences from the class attribute :py:attr:`sequences` - - :return: filtered sequences list in the attribute :py:attr:`sequences` and corresponding names. - :Example: - - >>> b = BaseSequence(4) - >>> b.sequences = ['KLLKLLKKLLKLLK', 'KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK', 'KLAKLAKKLAKLAK'] - >>> b.filter_duplicates() - >>> b.sequences - ['KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK'] - - .. versionadded:: v2.2.5 - """ - if not self.names: - self.names = ['Seq_' + str(i) for i in range(len(self.sequences))] - df = pd.DataFrame(list(zip(self.sequences, self.names)), columns=['Sequences', 'Names']) - df = df.drop_duplicates('Sequences', 'first') # keep first occurrence of duplicate - self.sequences = df['Sequences'].get_values().tolist() - self.names = df['Names'].get_values().tolist() - - def keep_natural_aa(self): - """Method to filter out sequences that do not contain natural amino acids. If the sequence contains a character - that is not in ``['A','C','D,'E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']``. - - :return: filtered sequence list in the attribute :py:attr:`sequences`. The other attributes are also filtered - accordingly (if present). - :Example: - - >>> b = BaseSequence(2) - >>> b.sequences = ['BBBsdflUasUJfBJ', 'GLFDIVKKVVGALGSL'] - >>> b.keep_natural_aa() - >>> b.sequences - ['GLFDIVKKVVGALGSL'] - """ - natural_aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', - 'Y'] - - seqs = [] - names = [] - - for i, s in enumerate(self.sequences): - seq = list(s.upper()) - if all(c in natural_aa for c in seq): - seqs.append(s.upper()) - if hasattr(self, 'names') and self.names: - names.append(self.names[i]) - - self.sequences = seqs - self.names = names - - def filter_aa(self, amino_acids): - """Method to filter out corresponding names and descriptor values of sequences with given amino acids in the - argument list *aminoacids*. - - :param amino_acids: {list} amino acids to be filtered - :return: filtered list of sequences names in the corresponding attributes. - :Example: - - >>> b = BaseSequence(3) - >>> b.sequences = ['AAALLLIIIKKK', 'CCEERRT', 'LLVVIIFFFQQ'] - >>> b.filter_aa(['C']) - >>> b.sequences - ['AAALLLIIIKKK', 'LLVVIIFFFQQ'] - """ - - pattern = re.compile('|'.join(amino_acids)) - seqs = [] - names = [] - - for i, s in enumerate(self.sequences): - if not pattern.search(s): - seqs.append(s) - if hasattr(self, 'names') and self.names: - names.append(self.names[i]) - - self.sequences = seqs - self.names = names - - def clean(self): - """Method to clean / clear / empty the attributes :py:attr:`sequences` and :py:attr:`names`. - - :return: freshly initialized, empty class attributes. - """ - self.__init__(self.seqnum, self.lenmin, self.lenmax) - - -class BaseDescriptor(object): - """ - Base class inheriting to both peptide descriptor classes :py:class:`modlamp.descriptors.GlobalDescriptor` and - :py:class:`modlamp.descriptors.PeptideDescriptor`. - """ - - def __init__(self, seqs): - """ - :param seqs: a ``.FASTA`` file with sequences, a list / array of sequences or a single sequence as string to - calculate the descriptor values for. - :return: initialized attributes :py:attr:`sequences` and :py:attr:`names`. - :Example: - - >>> AMP = BaseDescriptor('KLLKLLKKLLKLLK','pepCATS') - >>> AMP.sequences - ['KLLKLLKKLLKLLK'] - >>> seqs = BaseDescriptor('/Path/to/file.fasta', 'eisenberg') # load sequences from .fasta file - >>> seqs.sequences - ['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'...] - """ - if type(seqs) == list and seqs[0].isupper(): - self.sequences = [s.strip() for s in seqs] - self.names = [] - elif type(seqs) == np.ndarray and seqs[0].isupper(): - self.sequences = [s.strip() for s in seqs.tolist()] - self.names = [] - elif type(seqs) == str and seqs.isupper(): - self.sequences = [seqs.strip()] - self.names = [] - elif os.path.isfile(seqs): - if seqs.endswith('.fasta'): # read .fasta file - self.sequences, self.names = read_fasta(seqs) - elif seqs.endswith('.csv'): # read .csv file with sequences every line - with open(seqs) as f: - self.sequences = list() - cntr = 0 - self.names = [] - for line in f: - if line.isupper(): - self.sequences.append(line.strip()) - self.names.append('seq_' + str(cntr)) - cntr += 1 - else: - print("Sorry, currently only .fasta or .csv files can be read!") - else: - print("%s does not exist, is not a valid list of AA sequences or is not a valid sequence string" % seqs) - - self.descriptor = np.array([[]]) - self.target = np.array([], dtype='int') - self.scaler = None - self.featurenames = [] - - def read_fasta(self, filename): - """Method for loading sequences from a ``.FASTA`` formatted file into the attributes :py:attr:`sequences` and - :py:attr:`names`. - - :param filename: {str} ``.FASTA`` file with sequences and headers to read - :return: {list} sequences in the attribute :py:attr:`sequences` with corresponding sequence names in - :py:attr:`names`. - """ - self.sequences, self.names = read_fasta(filename) - - def save_fasta(self, filename, names=False): - """Method for saving sequences from :py:attr:`sequences` to a ``.FASTA`` formatted file. - - :param filename: {str} filename of the output ``.FASTA`` file - :param names: {bool} whether sequence names from self.names should be saved as sequence identifiers - :return: a FASTA formatted file containing the generated sequences - """ - if names: - save_fasta(filename, self.sequences, self.names) - else: - save_fasta(filename, self.sequences) - - def count_aa(self, scale='relative', average=False, append=False): - """Method for producing the amino acid distribution for the given sequences as a descriptor - - :param scale: {'absolute' or 'relative'} defines whether counts or frequencies are given for each AA - :param average: {boolean} whether the averaged amino acid counts for all sequences should be returned - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: the amino acid distributions for every sequence individually in the attribute :py:attr:`descriptor` - :Example: - - >>> AMP = PeptideDescriptor('ACDEFGHIKLMNPQRSTVWY') # aa_count() does not depend on the descriptor scale - >>> AMP.count_aa() - >>> AMP.descriptor - array([[ 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, ... ]]) - >>> AMP.descriptor.shape - (1, 20) - - .. seealso:: :py:func:`modlamp.core.count_aa()` - """ - desc = list() - for seq in self.sequences: - od = count_aas(seq, scale) - desc.append(list(od.values())) - - desc = np.array(desc) - self.featurenames = list(od.keys()) - - if append: - self.descriptor = np.hstack((self.descriptor, desc)) - elif average: - self.descriptor = np.mean(desc, axis=0) - else: - self.descriptor = desc - - def count_ngrams(self, n): - """Method for producing n-grams of all sequences in self.sequences - - :param n: {int or list of ints} defines whether counts or frequencies are given for each AA - :return: {dict} dictionary with n-grams as keys and their counts in the sequence as values in :py:attr:`descriptor` - :Example: - - >>> D = PeptideDescriptor('GLLDFLSLAALSLDKLVKKGALS') - >>> D.count_ngrams([2, 3]) - >>> D.descriptor - {'LS': 3, 'LD': 2, 'LSL': 2, 'AL': 2, ..., 'LVK': 1} - - .. seealso:: :py:func:`modlamp.core.count_ngrams()` - """ - ngrams = dict() - for seq in self.sequences: - d = count_ngrams(seq, n) - for k, v in d.items(): - if k in ngrams.keys(): - ngrams[k] += v - else: - ngrams[k] = v - self.descriptor = ngrams - - def feature_scaling(self, stype='standard', fit=True): - """Method for feature scaling of the calculated descriptor matrix. - - :param stype: {'standard' or 'minmax'} type of scaling to be used - :param fit: {boolean} defines whether the used scaler is first fitting on the data (True) or - whether the already fitted scaler in :py:attr:`scaler` should be used to transform (False). - :return: scaled descriptor values in :py:attr:`descriptor` - :Example: - - >>> D.descriptor - array([[0.155],[0.34],[0.16235294],[-0.08842105],[0.116]]) - >>> D.feature_scaling(type='minmax',fit=True) - array([[0.56818182],[1.],[0.5853447],[0.],[0.47714988]]) - """ - if stype in ['standard', 'minmax']: - if stype == 'standard': - self.scaler = StandardScaler() - elif stype == 'minmax': - self.scaler = MinMaxScaler() - - if fit: - self.descriptor = self.scaler.fit_transform(self.descriptor) - else: - self.descriptor = self.scaler.transform(self.descriptor) - else: - print("Unknown scaler type!\nAvailable: 'standard', 'minmax'") - - def feature_shuffle(self): - """Method for shuffling feature columns randomly. - - :return: descriptor matrix with shuffled feature columns in :py:attr:`descriptor` - :Example: - - >>> D.descriptor - array([[0.80685625,167.05234375,39.56818125,-0.26338667,155.16888667,33.48778]]) - >>> D.feature_shuffle() - array([[155.16888667,-0.26338667,167.05234375,0.80685625,39.56818125,33.48778]]) - """ - self.descriptor = shuffle(self.descriptor.transpose()).transpose() - - def sequence_order_shuffle(self): - """Method for shuffling sequence order in the attribute :py:attr:`sequences`. - - :return: sequences in :py:attr:`sequences` with shuffled order in the list. - :Example: - - >>> D.sequences - ['LILRALKGAARALKVA','VKIAKIALKIIKGLG','VGVRLIKGIGRVARGAI','LRGLRGVIRGGKAIVRVGK','GGKLVRLIARIGKGV'] - >>> D.sequence_order_shuffle() - >>> D.sequences - ['VGVRLIKGIGRVARGAI','LILRALKGAARALKVA','LRGLRGVIRGGKAIVRVGK','GGKLVRLIARIGKGV','VKIAKIALKIIKGLG'] - """ - self.sequences = shuffle(self.sequences) - - def random_selection(self, num): - """Method to randomly select a specified number of sequences (with names and descriptors if present) out of a given - descriptor instance. - - :param num: {int} number of entries to be randomly selected - :return: updated instance - :Example: - - >>> h = Helices(7, 28, 100) - >>> h.generate_helices() - >>> desc = PeptideDescriptor(h.sequences, 'eisenberg') - >>> desc.calculate_moment() - >>> len(desc.sequences) - 100 - >>> len(desc.descriptor) - 100 - >>> desc.random_selection(10) - >>> len(desc.descriptor) - 10 - >>> len(desc.descriptor) - 10 - - .. versionadded:: v2.2.3 - """ - - sel = np.random.choice(len(self.sequences), size=num, replace=False) - self.sequences = np.array(self.sequences)[sel].tolist() - if hasattr(self, 'descriptor') and self.descriptor.size: - self.descriptor = self.descriptor[sel] - if hasattr(self, 'names') and self.names: - self.names = np.array(self.names)[sel].tolist() - if hasattr(self, 'target') and self.target.size: - self.target = self.target[sel] - - def minmax_selection(self, iterations, distmetric='euclidean', seed=0): - """Method to select a specified number of sequences according to the minmax algorithm. - - :param iterations: {int} Number of sequences to retrieve. - :param distmetric: Distance metric to calculate the distances between the sequences in descriptor space. - Choose from 'euclidean' or 'minkowsky'. - :param seed: {int} Set a random seed for numpy to pick the first sequence. - :return: updated instance - - .. seealso:: **SciPy** http://docs.scipy.org/doc/scipy/reference/spatial.distance.html - """ - - # Storing M into pool, where selections get deleted - pool = self.descriptor # Store pool where selections get deleted - minmaxidx = list() # Store original indices of selections to return - - # Randomly selecting first peptide into the sele - np.random.seed(seed) - idx = int(np.random.random_integers(0, len(pool), 1)) - sele = pool[idx:idx + 1, :] - minmaxidx.append(int(*np.where(np.all(self.descriptor == pool[idx:idx + 1, :], axis=1)))) - - # Deleting peptide in selection from pool - pool = np.delete(pool, idx, axis=0) - - for i in range(iterations - 1): - # Calculating distance from sele to the rest of the peptides - dist = distance.cdist(pool, sele, distmetric) - - # Choosing maximal distances for every sele instance - maxidx = np.argmax(dist, axis=0) - maxcols = np.max(dist, axis=0) - - # Choosing minimal distance among the maximal distances - minmax = np.argmin(maxcols) - maxidx = int(maxidx[minmax]) - - # Adding it to selection and removing from pool - sele = np.append(sele, pool[maxidx:maxidx + 1, :], axis=0) - pool = np.delete(pool, maxidx, axis=0) - minmaxidx.append(int(*np.where(np.all(self.descriptor == pool[maxidx:maxidx + 1, :], axis=1)))) - - self.sequences = np.array(self.sequences)[minmaxidx].tolist() - if hasattr(self, 'descriptor') and self.descriptor.size: - self.descriptor = self.descriptor[minmaxidx] - if hasattr(self, 'names') and self.names: - self.names = np.array(self.names)[minmaxidx].tolist() - if hasattr(self, 'target') and self.target.size: - self.target = self.descriptor[minmaxidx] - - def filter_sequences(self, sequences): - """Method to filter out entries for given sequences in *sequences* out of a descriptor instance. All - corresponding attribute values of these sequences (e.g. in :py:attr:`descriptor`, :py:attr:`name`) are deleted - as well. The method returns an updated descriptor instance. - - :param sequences: {list} sequences to be filtered out of the whole instance, including corresponding data - :return: updated instance without filtered sequences - :Example: - - >>> sequences = ['KLLKLLKKLLKLLK', 'ACDEFGHIK', 'GLFDIVKKVV', 'GLFDIVKKVVGALG', 'GLFDIVKKVVGALGSL'] - >>> desc = PeptideDescriptor(sequences, 'pepcats') - >>> desc.calculate_crosscorr(7) - >>> len(desc.descriptor) - 5 - >>> desc.filter_sequences('KLLKLLKKLLKLLK') - >>> len(desc.descriptor) - 4 - >>> desc.sequences - ['ACDEFGHIK', 'GLFDIVKKVV', 'GLFDIVKKVVGALG', 'GLFDIVKKVVGALGSL'] - """ - indices = list() - if isinstance(sequences, str): # check if sequences is only one sequence string and convert it to a list - sequences = [sequences] - for s in sequences: # get indices of queried sequences - indices.append(self.sequences.index(s)) - - self.sequences = np.delete(np.array(self.sequences), indices, 0).tolist() - if hasattr(self, 'descriptor') and self.descriptor.size: - self.descriptor = np.delete(self.descriptor, indices, 0) - if hasattr(self, 'names') and self.names: - self.names = np.delete(np.array(self.names), indices, 0).tolist() - if hasattr(self, 'target') and self.target.size: - self.target = np.delete(self.target, indices, 0) - - def filter_values(self, values, operator='=='): - """Method to filter the descriptor matrix in the attribute :py:attr:`descriptor` for a given list of values (same - size as the number of features in the descriptor matrix!) The operator option tells the method whether to - filter for values equal, lower, higher ect. to the given values in the *values* array. - - :param values: {list} values to filter the attribute :py:attr:`descriptor` for - :param operator: {str} filter criterion, available the operators ``==``, ``<``, ``>``, ``<=``and ``>=``. - :return: descriptor matrix and updated sequences containing only entries with descriptor values given in - *values* in the corresponding attributes. - :Example: - - >>> desc.descriptor # desc = BaseDescriptor instance - array([[ 0.7666517 ], - [ 0.38373498]]) - >>> desc.filter_values([0.5], '<') - >>> desc.descriptor - array([[ 0.38373498]]) - """ - dim = self.descriptor.shape[1] - for d in range(dim): # for all the features in self.descriptor - if operator == '==': - indices = np.where(self.descriptor[:, d] == values[d])[0] - elif operator == '<': - indices = np.where(self.descriptor[:, d] < values[d])[0] - elif operator == '>': - indices = np.where(self.descriptor[:, d] > values[d])[0] - elif operator == '<=': - indices = np.where(self.descriptor[:, d] <= values[d])[0] - elif operator == '>=': - indices = np.where(self.descriptor[:, d] >= values[d])[0] - else: - raise KeyError('available operators: ``==``, ``<``, ``>``, ``<=``and ``>=``') - - # filter descriptor matrix, sequence list and names list according to obtained indices - self.sequences = np.array(self.sequences)[indices].tolist() - if hasattr(self, 'descriptor') and self.descriptor.size: - self.descriptor = self.descriptor[indices] - if hasattr(self, 'names') and self.names: - self.names = np.array(self.names)[indices].tolist() - if hasattr(self, 'target') and self.target.size: - self.target = self.target[indices] - - def filter_aa(self, amino_acids): - """Method to filter out corresponding names and descriptor values of sequences with given amino acids in the - argument list *aminoacids*. - - :param amino_acids: list of amino acids to be filtered - :return: filtered list of sequences, descriptor values, target values and names in the corresponding attributes. - :Example: - - >>> b = BaseSequence(3) - >>> b.sequences = ['AAALLLIIIKKK', 'CCEERRT', 'LLVVIIFFFQQ'] - >>> b.filter_aa(['C']) - >>> b.sequences - ['AAALLLIIIKKK', 'LLVVIIFFFQQ'] - """ - - pattern = re.compile('|'.join(amino_acids)) - seqs = [] - desc = [] - names = [] - target = [] - - for i, s in enumerate(self.sequences): - if not pattern.search(s): - seqs.append(s) - if hasattr(self, 'descriptor') and self.descriptor.size: - desc.append(self.descriptor[i]) - if hasattr(self, 'names') and self.names: - names.append(self.names[i]) - if hasattr(self, 'target') and self.target.size: - target.append(self.target[i]) - - self.sequences = seqs - self.names = names - self.descriptor = np.array(desc) - self.target = np.array(target, dtype='int') - - def filter_duplicates(self): - """Method to filter duplicates in the sequences from the class attribute :py:attr:`sequences` - - :return: filtered sequences list in the attribute :py:attr:`sequences` and corresponding names. - :Example: - - >>> b = BaseDescriptor(['KLLKLLKKLLKLLK', 'KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK', 'KLAKLAKKLAKLAK']) - >>> b.filter_duplicates() - >>> b.sequences - ['KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK'] - - .. versionadded:: v2.2.5 - """ - if not self.names: - self.names = ['Seq_' + str(i) for i in range(len(self.sequences))] - if not self.target: - self.target = [0] * len(self.sequences) - if not self.descriptor: - self.descriptor = np.zeros(len(self.sequences)) - df = pd.DataFrame(np.array([self.sequences, self.names, self.descriptor, self.target]).T, - columns=['Sequences', 'Names', 'Descriptor', 'Target']) - df = df.drop_duplicates('Sequences', 'first') # keep first occurrence of duplicate - self.sequences = df['Sequences'].get_values().tolist() - self.names = df['Names'].get_values().tolist() - self.descriptor = df['Descriptor'].get_values() - self.target = df['Target'].get_values() - - def keep_natural_aa(self): - """Method to filter out sequences that do not contain natural amino acids. If the sequence contains a character - that is not in ['A','C','D,'E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']. - - :return: filtered sequence list in the attribute :py:attr:`sequences`. The other attributes are also filtered - accordingly (if present). - :Example: - - >>> b = BaseSequence(2) - >>> b.sequences = ['BBBsdflUasUJfBJ', 'GLFDIVKKVVGALGSL'] - >>> b.keep_natural_aa() - >>> b.sequences - ['GLFDIVKKVVGALGSL'] - """ - - natural_aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', - 'Y'] - - seqs = [] - desc = [] - names = [] - target = [] - - for i, s in enumerate(self.sequences): - seq = list(s.upper()) - if all(c in natural_aa for c in seq): - seqs.append(s.upper()) - if hasattr(self, 'descriptor') and self.descriptor.size: - desc.append(self.descriptor[i]) - if hasattr(self, 'names') and self.names: - names.append(self.names[i]) - if hasattr(self, 'target') and self.target.size: - target.append(self.target[i]) - - self.sequences = seqs - self.names = names - self.descriptor = np.array(desc) - self.target = np.array(target, dtype='int') - - def load_descriptordata(self, filename, delimiter=",", targets=False, skip_header=0): - """Method to load any data file with sequences and descriptor values and save it to a new insatnce of the - class :class:`modlamp.descriptors.PeptideDescriptor`. - - .. note:: Headers are not considered. To skip initial lines in the file, use the *skip_header* option. - - :param filename: {str} filename of the data file to be loaded - :param delimiter: {str} column delimiter - :param targets: {boolean} whether last column in the file contains a target class vector - :param skip_header: {int} number of initial lines to skip in the file - :return: loaded sequences, descriptor values and targets in the corresponding attributes. - """ - data = np.genfromtxt(filename, delimiter=delimiter, skip_header=skip_header) - data = data[:, 1:] # skip sequences as they are "nan" when read as float - seqs = np.genfromtxt(filename, delimiter=delimiter, dtype="str") - seqs = seqs[:, 0] - if targets: - self.target = np.array(data[:, -1], dtype='int') - self.sequences = seqs - self.descriptor = data - - def save_descriptor(self, filename, delimiter=',', targets=None, header=None): - """Method to save the descriptor values to a .csv/.txt file - - :param filename: filename of the output file - :param delimiter: column delimiter - :param targets: target class vector to be added to descriptor (same length as :py:attr:`sequences`) - :param header: {str} header to be written at the beginning of the file (if ``None``: feature names are taken) - :return: output file with peptide names and descriptor values - """ - seqs = np.array(self.sequences, dtype='|S80')[:, np.newaxis] - ids = np.array(self.names, dtype='|S80')[:, np.newaxis] - if ids.shape == seqs.shape: - names = np.hstack((ids, seqs)) - else: - names = seqs - if targets and len(targets) == len(self.sequences): - target = np.array(targets)[:, np.newaxis] - data = np.hstack((names, self.descriptor, target)) - else: - data = np.hstack((names, self.descriptor)) - if not header: - featurenames = [['Sequence']] + self.featurenames - header = ', '.join([f[0] for f in featurenames]) - np.savetxt(filename, data, delimiter=delimiter, fmt='%s', header=header) - - -def load_scale(scalename): - """Method to load scale values for a given amino acid scale - - :param scalename: amino acid scale name, for available scales see the - :class:`modlamp.descriptors.PeptideDescriptor()` documentation. - :return: amino acid scale values in dictionary format. - """ - # predefined amino acid scales dictionary - scales = { - 'aasi': {'A': [1.89], 'C': [1.73], 'D': [3.13], 'E': [3.14], 'F': [1.53], 'G': [2.67], 'H': [3], 'I': [1.97], - 'K': [2.28], 'L': [1.74], 'M': [2.5], 'N': [2.33], 'P': [0.22], 'Q': [3.05], 'R': [1.91], 'S': [2.14], - 'T': [2.18], 'V': [2.37], 'W': [2], 'Y': [2.01]}, - 'abhprk': {'A': [0, 0, 0, 0, 0, 0], 'C': [0, 0, 0, 0, 0, 0], 'D': [1, 0, 0, 1, 0, 0], 'E': [1, 0, 0, 1, 0, 0], - 'F': [0, 0, 1, 0, 1, 0], 'G': [0, 0, 0, 0, 0, 0], 'H': [0, 0, 0, 1, 1, 0], 'I': [0, 0, 1, 0, 0, 0], - 'K': [0, 1, 0, 1, 0, 0], 'L': [0, 0, 1, 0, 0, 0], 'M': [0, 0, 1, 0, 0, 0], 'N': [0, 0, 0, 1, 0, 0], - 'P': [0, 0, 0, 0, 0, 1], 'Q': [0, 0, 0, 1, 0, 0], 'R': [0, 1, 0, 1, 0, 0], 'S': [0, 0, 0, 1, 0, 0], - 'T': [0, 0, 0, 1, 0, 0], 'V': [0, 0, 1, 0, 0, 0], 'W': [0, 0, 1, 0, 1, 0], 'Y': [0, 0, 0, 1, 1, 0]}, - 'argos': {'I': [0.77], 'F': [1.2], 'V': [0.14], 'L': [2.3], 'W': [0.07], 'M': [2.3], 'A': [0.64], 'G': [-0.48], - 'C': [0.25], 'Y': [-0.41], 'P': [-0.31], 'T': [-0.13], 'S': [-0.25], 'H': [-0.87], 'E': [-0.94], - 'N': [-0.89], 'Q': [-0.61], 'D': [-1], 'K': [-1], 'R': [-0.68]}, - 'bulkiness': {'A': [0.443], 'C': [0.551], 'D': [0.453], 'E': [0.557], 'F': [0.898], 'G': [0], 'H': [0.563], - 'I': [0.985], 'K': [0.674], 'L': [0.985], 'M': [0.703], 'N': [0.516], 'P': [0.768], 'Q': [0.605], - 'R': [0.596], 'S': [0.332], 'T': [0.677], 'V': [0.995], 'W': [1], 'Y': [0.801]}, - 'charge_phys': {'A': [0.], 'C': [-.1], 'D': [-1.], 'E': [-1.], 'F': [0.], 'G': [0.], 'H': [0.1], - 'I': [0.], 'K': [1.], 'L': [0.], 'M': [0.], 'N': [0.], 'P': [0.], 'Q': [0.], - 'R': [1.], 'S': [0.], 'T': [0.], 'V': [0.], 'W': [0.], 'Y': [0.]}, - 'charge_acid': {'A': [0.], 'C': [-.1], 'D': [-1.], 'E': [-1.], 'F': [0.], 'G': [0.], 'H': [1.], - 'I': [0.], 'K': [1.], 'L': [0.], 'M': [0.], 'N': [0.], 'P': [0.], 'Q': [0.], - 'R': [1.], 'S': [0.], 'T': [0.], 'V': [0.], 'W': [0.], 'Y': [0.]}, - 'cougar': {'A': [0.25, 0.62, 1.89], 'C': [0.208, 0.29, 1.73], 'D': [0.875, -0.9, 3.13], - 'E': [0.833, -0.74, 3.14], 'F': [0.042, 1.2, 1.53], 'G': [1, 0.48, 2.67], 'H': [0.083, -0.4, 3], - 'I': [0.667, 1.4, 1.97], 'K': [0.708, -1.5, 2.28], 'L': [0.292, 1.1, 1.74], 'M': [0, 0.64, 2.5], - 'N': [0.667, -0.78, 2.33], 'P': [0.875, 0.12, 0.22], 'Q': [0.792, -0.85, 3.05], - 'R': [0.958, -2.5, 1.91], 'S': [0.875, -0.18, 2.14], 'T': [0.583, -0.05, 2.18], - 'V': [0.375, 1.1, 2.37], 'W': [0.042, 0.81, 2], 'Y': [0.5, 0.26, 2.01]}, - 'eisenberg': {'I': [1.4], 'F': [1.2], 'V': [1.1], 'L': [1.1], 'W': [0.81], 'M': [0.64], 'A': [0.62], - 'G': [0.48], 'C': [0.29], 'Y': [0.26], 'P': [0.12], 'T': [-0.05], 'S': [-0.18], 'H': [-0.4], - 'E': [-0.74], 'N': [-0.78], 'Q': [-0.85], 'D': [-0.9], 'K': [-1.5], 'R': [-2.5]}, - 'ez': {'A': [-0.29, 10.22, 4.67], 'C': [0.95, 13.69, 5.77], 'D': [1.19, 14.25, 8.98], 'E': [1.3, 14.66, 4.16], - 'F': [-0.8, 19.67, 7.12], 'G': [-0.01, 13.86, 6], 'H': [0.75, 12.26, 2.77], 'I': [-0.56, 14.34, 10.69], - 'K': [1.66, 11.11, 2.09], 'L': [-0.64, 17.34, 8.61], 'M': [-0.28, 18.04, 7.13], 'N': [0.89, 12.78, 6.28], - 'P': [0.83, 18.09, 3.53], 'Q': [1.21, 10.46, 2.59], 'R': [1.55, 9.34, 4.68], 'S': [0.1, 13.86, 6], - 'T': [0.01, 13.86, 6], 'V': [-0.47, 11.35, 4.97], 'W': [-0.85, 11.65, 7.2], 'Y': [-0.42, 13.04, 6.2]}, - 'flexibility': {'A': [0.25], 'C': [0.208], 'D': [0.875], 'E': [0.833], 'F': [0.042], 'G': [1], 'H': [0.083], - 'I': [0.667], 'K': [0.708], 'L': [0.292], 'M': [0.], 'N': [0.667], 'P': [0.875], 'Q': [0.792], - 'R': [0.958], 'S': [0.875], 'T': [0.583], 'V': [0.375], 'W': [0.042], 'Y': [0.5]}, - 'grantham': {'A': [0, 8.1, 31], 'C': [2.75, 5.5, 55], 'D': [1.38, 13.0, 54], 'E': [0.92, 12.3, 83], - 'F': [0, 5.2, 132], 'G': [0.74, 9.0, 3], 'H': [0.58, 10.4, 96], 'I': [0, 5.2, 111], - 'K': [0.33, 11.3, 119], 'L': [0, 4.9, 111], 'M': [0, 5.7, 105], 'N': [1.33, 11.6, 56], - 'P': [0.39, 8.0, 32.5], 'Q': [0.89, 10.5, 85], 'R': [0.65, 10.5, 124], 'S': [1.42, 9.2, 32], - 'T': [0.71, 8.6, 61], 'V': [0, 5.9, 84], 'W': [0.13, 5.4, 170], 'Y': [0.20, 6.2, 136]}, - 'gravy': {'I': [4.5], 'V': [4.2], 'L': [3.8], 'F': [2.8], 'C': [2.5], 'M': [1.9], 'A': [1.8], 'G': [-0.4], - 'T': [-0.7], 'W': [-0.9], 'S': [-0.8], 'Y': [-1.3], 'P': [-1.6], 'H': [-3.2], 'E': [-3.5], - 'Q': [-3.5], 'D': [-3.5], 'N': [-3.5], 'K': [-3.9], 'R': [-4.5]}, - 'hopp-woods': {'A': [-0.5], 'C': [-1], 'D': [3], 'E': [3], 'F': [-2.5], 'G': [0], 'H': [-0.5], 'I': [-1.8], - 'K': [3], 'L': [-1.8], 'M': [-1.3], 'N': [0.2], 'P': [0], 'Q': [0.2], 'R': [3], 'S': [0.3], - 'T': [-0.4], 'V': [-1.5], 'W': [-3.4], 'Y': [-2.3]}, - 'isaeci': {'A': [62.9, 0.05], 'C': [78.51, 0.15], 'D': [18.46, 1.25], 'E': [30.19, 1.31], 'F': [189.42, 0.14], - 'G': [19.93, 0.02], 'H': [87.38, 0.56], 'I': [149.77, 0.09], 'K': [102.78, 0.53], 'L': [154.35, 0.1], - 'M': [132.22, 0.34], 'N': [19.53, 1.36], 'P': [122.35, 0.16], 'Q': [17.87, 1.31], 'R': [52.98, 1.69], - 'S': [19.75, 0.56], 'T': [59.44, 0.65], 'V': [120.91, 0.07], 'W': [179.16, 1.08], - 'Y': [132.16, 0.72]}, - 'janin': {'I': [1.2], 'F': [0.87], 'V': [1], 'L': [0.87], 'W': [0.59], 'M': [0.73], 'A': [0.59], 'G': [0.59], - 'C': [1.4], 'Y': [-0.4], 'P': [-0.26], 'T': [-0.12], 'S': [0.02], 'H': [0.02], 'E': [-0.83], - 'N': [-0.55], 'Q': [-0.83], 'D': [-0.69], 'K': [-2.4], 'R': [-1.8]}, - 'kytedoolittle': {'I': [1.7], 'F': [1.1], 'V': [1.6], 'L': [1.4], 'W': [-0.14], 'M': [0.8], 'A': [0.77], - 'G': [0.03], 'C': [1], 'Y': [-0.27], 'P': [-0.37], 'T': [-0.07], 'S': [-0.1], 'H': [-0.91], - 'E': [-1], 'N': [-1], 'Q': [-1], 'D': [-1], 'K': [-1.1], 'R': [-1.3]}, - 'levitt_alpha': {'A': [1.29], 'C': [1.11], 'D': [1.04], 'E': [1.44], 'F': [1.07], 'G': [0.56], 'H': [1.22], - 'I': [0.97], 'K': [1.23], 'L': [1.3], 'M': [1.47], 'N': [0.9], 'P': [0.52], 'Q': [1.27], - 'R': [0.96], 'S': [0.82], 'T': [0.82], 'V': [0.91], 'W': [0.99], 'Y': [0.72]}, - 'mss': {'A': [13.02], 'C': [23.7067], 'D': [22.02], 'E': [20.0233], 'F': [23.5288], 'G': [1.01], 'H': [23.5283], - 'I': [22.3611], 'K': [18.9756], 'L': [19.6944], 'M': [21.92], 'N': [21.8567], 'P': [19.0242], - 'Q': [19.9689], 'R': [19.0434], 'S': [18.3533], 'T': [22.3567], 'V': [21.0267], 'W': [26.1975], - 'Y': [24.1954]}, - 'msw': {'A': [-0.73, 0.2, -0.62], 'C': [-0.66, 0.26, -0.27], 'D': [0.11, -1, -0.96], 'E': [0.24, -0.39, -0.04], - 'F': [0.76, 0.85, -0.34], 'G': [-0.31, -0.28, -0.75], 'H': [0.84, 0.67, -0.78], - 'I': [-0.91, 0.83, -0.25], 'K': [-0.51, 0.08, 0.6], 'L': [-0.74, 0.72, -0.16], 'M': [-0.7, 1, -0.32], - 'N': [0.14, 0.2, -0.66], 'P': [-0.43, 0.73, -0.6], 'Q': [0.3, 1, -0.3], 'R': [-0.22, 0.27, 1], - 'S': [-0.8, 0.61, -1], 'T': [-0.58, 0.85, -0.89], 'V': [-1, 0.79, -0.58], 'W': [1, 0.98, -0.47], - 'Y': [0.97, 0.66, -0.16]}, - 'pepcats': {'A': [1, 0, 0, 0, 0, 0], 'C': [1, 0, 1, 1, 0, 0], 'D': [0, 0, 1, 0, 0, 1], 'E': [0, 0, 1, 0, 0, 1], - 'F': [1, 1, 0, 0, 0, 0], 'G': [0, 0, 0, 0, 0, 0], 'H': [1, 1, 0, 1, 1, 0], 'I': [1, 0, 0, 0, 0, 0], - 'K': [1, 0, 0, 1, 1, 0], 'L': [1, 0, 0, 0, 0, 0], 'M': [1, 0, 1, 0, 0, 0], 'N': [0, 0, 1, 1, 0, 0], - 'P': [1, 0, 0, 0, 0, 0], 'Q': [0, 0, 1, 1, 0, 0], 'R': [1, 0, 0, 1, 1, 0], 'S': [0, 0, 1, 1, 0, 0], - 'T': [0, 0, 1, 1, 0, 0], 'V': [1, 0, 0, 0, 0, 0], 'W': [1, 1, 0, 1, 0, 0], 'Y': [1, 1, 1, 1, 0, 0]}, - 'peparc': {'A': [1, 0, 0, 0, 0], 'C': [0, 1, 0, 0, 0], 'D': [0, 1, 0, 1, 0], 'E': [0, 1, 0, 1, 0], - 'F': [1, 0, 0, 0, 0], 'G': [0, 0, 0, 0, 0], 'H': [0, 1, 1, 0, 0], 'I': [1, 0, 0, 0, 0], - 'K': [0, 1, 1, 0, 0], 'L': [1, 0, 0, 0, 0], 'M': [1, 0, 0, 0, 0], 'N': [0, 1, 0, 0, 0], - 'P': [0, 0, 0, 0, 1], 'Q': [0, 1, 0, 0, 0], 'R': [0, 1, 1, 0, 0], 'S': [0, 1, 0, 0, 0], - 'T': [0, 1, 0, 0, 0], 'V': [1, 0, 0, 0, 0], 'W': [1, 0, 0, 0, 0], 'Y': [1, 0, 0, 0, 0]}, - 'polarity': {'A': [0.395], 'C': [0.074], 'D': [1.], 'E': [0.914], 'F': [0.037], 'G': [0.506], 'H': [0.679], - 'I': [0.037], 'K': [0.79], 'L': [0.], 'M': [0.099], 'N': [0.827], 'P': [0.383], 'Q': [0.691], - 'R': [0.691], 'S': [0.531], 'T': [0.457], 'V': [0.123], 'W': [0.062], 'Y': [0.16]}, - 'ppcali': { - 'A': [0.070781, 0.036271, 2.042, 0.083272, 0.69089, 0.15948, -0.80893, 0.24698, 0.86525, 0.68563, -0.24665, - 0.61314, -0.53343, -0.50878, -1.3646, 2.2679, -1.5644, -0.75043, -0.65875], - 'C': [0.61013, -0.93043, -0.85983, -2.2704, 1.5877, -2.0066, -0.30314, 1.2544, -0.2832, -1.2844, -0.73449, - -0.11235, -0.41152, -0.0050164, 0.28307, 0.20522, -0.021084, -0.15627, -0.32689], - 'D': [-1.3215, 0.24063, -0.032754, -0.37863, 1.2051, 1.0001, 2.1827, 0.19212, -0.60529, 0.37639, -0.46451, - -0.46788, 1.4077, -2.1661, 0.72604, -0.12332, -0.8243, -0.082989, 0.053476], - 'E': [-0.87713, 1.4905, 1.0755, 0.35944, 1.567, 0.41365, 1.0944, 0.72634, -0.74957, 0.038939, 0.075057, - 0.78637, -1.4543, 1.6667, -0.097439, -0.24293, 1.7687, 0.36174, -0.11585], - 'F': [1.3557, -0.10336, -0.4309, 0.41269, -0.083356, 0.83783, 0.095381, -0.65222, -0.3119, 0.43293, -1.0011, - -0.66855, -0.10242, 1.2066, 2.6234, 1.9981, -0.25016, 0.71979, 0.21569], - 'G': [-1.0818, -2.1561, 0.77082, -0.92747, -1.0748, 1.7997, -1.3708, 1.279, -1.2098, 0.46065, 0.43076, - 0.20037, -0.2302, 0.2646, 0.57149, -0.68432, 0.19341, -0.061606, -0.08071], - 'H': [-0.050161, 0.69246, -0.88397, -0.64601, 0.24622, 0.10487, -1.1317, -2.3661, -0.89918, 0.46391, - -0.62359, 2.5478, -0.34737, -0.52062, 0.17522, -0.88648, -0.4755, 0.023187, -0.28261], - 'I': [1.4829, -0.46435, 0.50189, 0.55724, -0.51535, -0.29914, 0.97236, -0.15793, -0.98246, -0.54347, - 0.97806, 0.37577, 1.618, 0.62323, -0.59359, -0.35483, -0.085017, 0.55825, -2.7542], - 'K': [-0.85344, 1.529, 0.27747, 0.32993, -1.1786, -0.16633, -1.0459, 0.44621, 0.41027, -2.5318, 0.91329, - 0.53385, 0.61417, -1.111, 1.1323, 0.95105, 0.76769, -0.016115, 0.054995], - 'L': [1.2857, 0.039488, 1.5378, 0.87969, -0.21419, 0.40389, -0.20426, -0.14351, 0.61024, -1.1927, -2.2149, - -0.84248, -0.5061, -0.48548, 0.10791, -2.1503, -0.12006, -0.60222, 0.26546], - 'M': [1.137, 0.64388, 0.13724, -0.2988, 1.2288, 0.24981, -1.6427, -0.75868, -0.54902, 1.0571, 1.272, - -1.9104, 0.70919, -0.93575, -0.6314, -0.079654, 1.634, -0.0021923, 0.49825], - 'N': [-1.084, -0.176, -0.47062, -0.92245, -0.32953, 0.74278, 0.34551, -1.4605, 0.25219, -1.2107, -0.59978, - -0.79183, 1.3268, 1.9839, -1.6137, 0.5333, 0.033889, -1.0331, 0.83019], - 'P': [-1.1823, -1.6911, -1.1331, 3.073, 1.1942, -0.93426, -0.72985, -0.042441, -0.19264, -0.21603, -0.1239, - 0.054016, 0.15241, -0.019691, -0.20543, 0.10206, 0.07671, -0.081968, 0.20348], - 'Q': [-0.57747, 0.97452, -0.077547, -0.0033488, 0.17184, -0.52537, -0.27362, -0.1366, 0.2057, -0.013066, - 1.8834, -1.2736, -0.84991, 1.0445, 0.69027, -1.2866, -2.6776, 0.1683, 0.086105], - 'R': [-0.62245, 1.545, -0.61966, 0.19057, -1.7485, -1.3909, -0.47526, 1.3938, -0.84556, 1.7344, -1.6516, - -0.52678, 0.6791, 0.24374, -0.62551, -0.0028271, -0.053884, 0.14926, -0.17232], - 'S': [-0.86409, -0.77147, 0.38542, -0.59389, -0.53313, -0.47585, 0.31966, -0.89716, 1.8029, 0.26431, - -0.23173, -0.37626, -0.47349, -0.42878, -0.47297, -0.079826, 0.57043, 3.2057, -0.18413], - 'T': [-0.33027, -0.57447, 0.18653, -0.28941, -0.62681, -1.0737, 0.80363, -0.59525, 1.8786, 1.3971, 0.63929, - 0.21281, -0.067048, 0.096271, 1.323, -0.36173, 1.2261, -2.2771, -0.65412], - 'V': [1.1675, -0.61554, 0.95405, 0.11662, -0.74473, -1.1482, 1.1309, 0.12079, -0.77171, 0.18597, 0.93442, - 1.201, 0.3826, -0.091573, -0.31269, 0.074367, -0.22946, 0.24322, 2.9836], - 'W': [1.1881, 0.43789, -1.7915, 0.138, 0.43088, 1.6467, -0.11987, 1.7369, 2.0818, 0.33122, 0.31829, 1.1586, - 0.67649, 0.30819, -0.55772, -0.54491, -0.17969, 0.24477, 0.38674], - 'Y': [0.54671, -0.1468, -1.5688, 0.19001, -1.2736, 0.66162, 1.1614, -0.18614, -0.70654, -0.43634, 0.44775, - -0.71366, -2.5907, -1.1649, -1.1576, 0.66572, 0.21019, -0.61016, -0.34844]}, - 'refractivity': {'A': [0.102045615], 'C': [0.841053374], 'D': [0.282153774], 'E': [0.405831178], - 'F': [0.691276746], 'G': [0], 'H': [0.512814484], 'I': [0.448154244], 'K': [0.50058782], - 'L': [0.441570656], 'M': [0.508817305], 'N': [0.282153774], 'P': [0.256995062], - 'Q': [0.405831178], 'R': [0.626851634], 'S': [0.149306372], 'T': [0.258876087], - 'V': [0.327298378], 'W': [1], 'Y': [0.741359041]}, - 't_scale': {'A': [-8.4, -8.01, -3.73, -3.65, -6.12, -1.59, 1.56], - 'C': [-2.44, -1.96, 0.93, -2.35, 1.31, 2.29, -1.52], - 'D': [-6.84, -0.94, 17.68, -0.03, 3.44, 9.07, 4.32], - 'E': [-6.5, 16.2, 17.28, 3.11, -4.75, -2.54, 4.72], - 'F': [21.59, -5.73, 1.03, -3.3, 2.64, -5.02, 1.7], - 'G': [-8.48, -10.37, -5.14, -6.51, -11.84, -3.6, 2.01], - 'H': [15.28, -3.67, 6.72, -6.38, 4.12, -1.55, -2.85], - 'I': [-2.97, 4.64, -0.77, 11, 3.26, -4.36, -7.88], - 'K': [2.7, 13.46, -14.03, -2.55, 2.77, 0.15, 3.19], - 'L': [2.61, 5.96, 1.97, 2.59, -4.77, -4.84, -5.44], - 'M': [3.38, 12.43, -4.77, 0.45, -1.55, -0.6, 3.26], - 'N': [-3.11, -1.22, 6.26, -9.38, 9.94, 7.66, -4.81], - 'P': [-5.35, -9.07, -1.52, -8.79, -8.73, 4.29, -9.91], - 'Q': [-5.31, 15.64, 8.44, 1.03, -4.32, -4.4, -0.52], - 'R': [-2.27, 18.9, -18.24, -3.47, 3.03, 6.64, 0.45], - 'S': [-15.88, -11.21, -2.44, -3.61, 3.46, -0.37, 8.98], - 'T': [-17.81, -13.64, -5.19, 10.57, 6.91, -4.43, 3.49], - 'V': [-5.8, -6.15, -2.26, 9.87, 5.28, -1.49, -7.54], - 'W': [21.68, -8.78, -2.53, 15.53, -8.15, 11.98, 3.23], - 'Y': [23.9, -6.47, 0.31, -4.14, 4.08, -7.28, 3.59]}, - 'tm_tend': {'A': [0.38], 'C': [-0.3], 'D': [-3.27], 'E': [-2.9], 'F': [1.98], 'G': [-0.19], 'H': [-1.44], - 'I': [1.97], 'K': [-3.46], 'L': [1.82], 'M': [1.4], 'N': [-1.62], 'P': [-1.44], 'Q': [-1.84], - 'R': [-2.57], 'S': [-0.53], 'T': [-0.32], 'V': [1.46], 'W': [1.53], 'Y': [0.49]}, - 'z3': {'A': [0.07, -1.73, 0.09], 'C': [0.71, -0.97, 4.13], 'D': [3.64, 1.13, 2.36], 'E': [3.08, 0.39, -0.07], - 'F': [-4.92, 1.3, 0.45], 'G': [2.23, -5.36, 0.3], 'H': [2.41, 1.74, 1.11], 'I': [-4.44, -1.68, -1.03], - 'K': [2.84, 1.41, -3.14], 'L': [-4.19, -1.03, -0.98], 'M': [-2.49, -0.27, -0.41], - 'N': [3.22, 1.45, 0.84], 'P': [-1.22, 0.88, 2.23], 'Q': [2.18, 0.53, -1.14], 'R': [2.88, 2.52, -3.44], - 'S': [1.96, -1.63, 0.57], 'T': [0.92, -2.09, -1.4], 'V': [-2.69, -2.53, -1.29], 'W': [-4.75, 3.65, 0.85], - 'Y': [-1.39, 2.32, 0.01]}, - 'z5': {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} - } - if scalename == 'all': - d = {'I': [], 'F': [], 'V': [], 'L': [], 'W': [], 'M': [], 'A': [], 'G': [], 'C': [], 'Y': [], 'P': [], - 'T': [], 'S': [], 'H': [], 'E': [], 'N': [], 'Q': [], 'D': [], 'K': [], 'R': []} - for scale in scales.keys(): - for k, v in scales[scale].items(): - d[k].extend(v) - return 'all', d - - elif scalename == 'instability': - d = { - "A": {"A": 1.0, "C": 44.94, "E": 1.0, "D": -7.49, "G": 1.0, "F": 1.0, "I": 1.0, "H": -7.49, "K": 1.0, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 1.0, "P": 20.26, "S": 1.0, "R": 1.0, "T": 1.0, "W": 1.0, "V": 1.0, - "Y": 1.0}, - "C": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 20.26, "G": 1.0, "F": 1.0, "I": 1.0, "H": 33.6, "K": 1.0, - "M": 33.6, "L": 20.26, "N": 1.0, "Q": -6.54, "P": 20.26, "S": 1.0, "R": 1.0, "T": 33.6, "W": 24.68, - "V": -6.54, "Y": 1.0}, - "E": {"A": 1.0, "C": 44.94, "E": 33.6, "D": 20.26, "G": 1.0, "F": 1.0, "I": 20.26, "H": -6.54, "K": 1.0, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 20.26, "P": 20.26, "S": 20.26, "R": 1.0, "T": 1.0, "W": -14.03, - "V": 1.0, "Y": 1.0}, - "D": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 1.0, "G": 1.0, "F": -6.54, "I": 1.0, "H": 1.0, "K": -7.49, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 1.0, "P": 1.0, "S": 20.26, "R": -6.54, "T": -14.03, "W": 1.0, - "V": 1.0, "Y": 1.0}, - "G": {"A": -7.49, "C": 1.0, "E": -6.54, "D": 1.0, "G": 13.34, "F": 1.0, "I": -7.49, "H": 1.0, "K": -7.49, - "M": 1.0, "L": 1.0, "N": -7.49, "Q": 1.0, "P": 1.0, "S": 1.0, "R": 1.0, "T": -7.49, "W": 13.34, - "V": 1.0, "Y": -7.49}, - "F": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 13.34, "G": 1.0, "F": 1.0, "I": 1.0, "H": 1.0, "K": -14.03, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 1.0, "P": 20.26, "S": 1.0, "R": 1.0, "T": 1.0, "W": 1.0, "V": 1.0, - "Y": 33.601}, - "I": {"A": 1.0, "C": 1.0, "E": 44.94, "D": 1.0, "G": 1.0, "F": 1.0, "I": 1.0, "H": 13.34, "K": -7.49, - "M": 1.0, "L": 20.26, "N": 1.0, "Q": 1.0, "P": -1.88, "S": 1.0, "R": 1.0, "T": 1.0, "W": 1.0, - "V": -7.49, "Y": 1.0}, - "H": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 1.0, "G": -9.37, "F": -9.37, "I": 44.94, "H": 1.0, "K": 24.68, - "M": 1.0, "L": 1.0, "N": 24.68, "Q": 1.0, "P": -1.88, "S": 1.0, "R": 1.0, "T": -6.54, "W": -1.88, - "V": 1.0, "Y": 44.94}, - "K": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 1.0, "G": -7.49, "F": 1.0, "I": -7.49, "H": 1.0, "K": 1.0, - "M": 33.6, "L": -7.49, "N": 1.0, "Q": 24.64, "P": -6.54, "S": 1.0, "R": 33.6, "T": 1.0, "W": 1.0, - "V": -7.49, "Y": 1.0}, - "M": {"A": 13.34, "C": 1.0, "E": 1.0, "D": 1.0, "G": 1.0, "F": 1.0, "I": 1.0, "H": 58.28, "K": 1.0, - "M": -1.88, "L": 1.0, "N": 1.0, "Q": -6.54, "P": 44.94, "S": 44.94, "R": -6.54, "T": -1.88, "W": 1.0, - "V": 1.0, "Y": 24.68}, - "L": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 1.0, "G": 1.0, "F": 1.0, "I": 1.0, "H": 1.0, "K": -7.49, "M": 1.0, - "L": 1.0, "N": 1.0, "Q": 33.6, "P": 20.26, "S": 1.0, "R": 20.26, "T": 1.0, "W": 24.68, "V": 1.0, - "Y": 1.0}, - "N": {"A": 1.0, "C": -1.88, "E": 1.0, "D": 1.0, "G": -14.03, "F": -14.03, "I": 44.94, "H": 1.0, "K": 24.68, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": -6.54, "P": -1.88, "S": 1.0, "R": 1.0, "T": -7.49, "W": -9.37, - "V": 1.0, "Y": 1.0}, - "Q": {"A": 1.0, "C": -6.54, "E": 20.26, "D": 20.26, "G": 1.0, "F": -6.54, "I": 1.0, "H": 1.0, "K": 1.0, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 20.26, "P": 20.26, "S": 44.94, "R": 1.0, "T": 1.0, "W": 1.0, - "V": -6.54, "Y": -6.54}, - "P": {"A": 20.26, "C": -6.54, "E": 18.38, "D": -6.54, "G": 1.0, "F": 20.26, "I": 1.0, "H": 1.0, "K": 1.0, - "M": -6.54, "L": 1.0, "N": 1.0, "Q": 20.26, "P": 20.26, "S": 20.26, "R": -6.54, "T": 1.0, "W": -1.88, - "V": 20.26, "Y": 1.0}, - "S": {"A": 1.0, "C": 33.6, "E": 20.26, "D": 1.0, "G": 1.0, "F": 1.0, "I": 1.0, "H": 1.0, "K": 1.0, "M": 1.0, - "L": 1.0, "N": 1.0, "Q": 20.26, "P": 44.94, "S": 20.26, "R": 20.26, "T": 1.0, "W": 1.0, "V": 1.0, - "Y": 1.0}, - "R": {"A": 1.0, "C": 1.0, "E": 1.0, "D": 1.0, "G": -7.49, "F": 1.0, "I": 1.0, "H": 20.26, "K": 1.0, - "M": 1.0, "L": 1.0, "N": 13.34, "Q": 20.26, "P": 20.26, "S": 44.94, "R": 58.28, "T": 1.0, "W": 58.28, - "V": 1.0, "Y": -6.54}, - "T": {"A": 1.0, "C": 1.0, "E": 20.26, "D": 1.0, "G": -7.49, "F": 13.34, "I": 1.0, "H": 1.0, "K": 1.0, - "M": 1.0, "L": 1.0, "N": -14.03, "Q": -6.54, "P": 1.0, "S": 1.0, "R": 1.0, "T": 1.0, "W": -14.03, - "V": 1.0, "Y": 1.0}, - "W": {"A": -14.03, "C": 1.0, "E": 1.0, "D": 1.0, "G": -9.37, "F": 1.0, "I": 1.0, "H": 24.68, "K": 1.0, - "M": 24.68, "L": 13.34, "N": 13.34, "Q": 1.0, "P": 1.0, "S": 1.0, "R": 1.0, "T": -14.03, "W": 1.0, - "V": -7.49, "Y": 1.0}, - "V": {"A": 1.0, "C": 1.0, "E": 1.0, "D": -14.03, "G": -7.49, "F": 1.0, "I": 1.0, "H": 1.0, "K": -1.88, - "M": 1.0, "L": 1.0, "N": 1.0, "Q": 1.0, "P": 20.26, "S": 1.0, "R": 1.0, "T": -7.49, "W": 1.0, - "V": 1.0, "Y": -6.54}, - "Y": {"A": 24.68, "C": 1.0, "E": -6.54, "D": 24.68, "G": -7.49, "F": 1.0, "I": 1.0, "H": 13.34, "K": 1.0, - "M": 44.94, "L": 1.0, "N": 1.0, "Q": 1.0, "P": 13.34, "S": 1.0, "R": -15.91, "T": -7.49, "W": -9.37, - "V": 1.0, "Y": 13.34}} - return 'instability', d - - else: - return scalename, scales[scalename] - - -def read_fasta(inputfile): - """Method for loading sequences from a FASTA formatted file into :py:attr:`sequences` & :py:attr:`names`. - This method is used by the base class :class:`modlamp.descriptors.PeptideDescriptor` if the input is a FASTA file. - - :param inputfile: .fasta file with sequences and headers to read - :return: list of sequences in the attribute :py:attr:`sequences` with corresponding sequence names in - :py:attr:`names`. - """ - names = list() # list for storing names - sequences = list() # list for storing sequences - seq = str() - with open(inputfile) as f: - all = f.readlines() - last = all[-1] - for line in all: - if line.startswith('>'): - names.append(line.split(' ')[0][1:].strip()) # add FASTA name without description as molecule name - sequences.append(seq.strip()) - seq = str() - elif line == last: - seq += line.strip() # remove potential white space - sequences.append(seq.strip()) - else: - seq += line.strip() # remove potential white space - return sequences[1:], names - - -def save_fasta(filename, sequences, names=None): - """Method for saving sequences in the instance :py:attr:`sequences` to a file in FASTA format. - - :param filename: {str} output filename (ending .fasta) - :param sequences: {list} sequences to be saved to file - :param names: {list} whether sequence names from self.names should be saved as sequence identifiers - :return: a FASTA formatted file containing the generated sequences - """ - if os.path.exists(filename): - os.remove(filename) # remove outputfile, it it exists - - with open(filename, 'w') as o: - for n, seq in enumerate(sequences): - if names: - o.write('>' + str(names[n]) + '\n') - else: - o.write('>Seq_' + str(n) + '\n') - o.write(seq + '\n') - - -def aa_weights(): - """Function holding molecular weight data on all natural amino acids. - - :return: dictionary with amino acid letters and corresponding weights - - .. versionadded:: v2.4.1 - """ - weights = {'A': 89.093, 'C': 121.158, 'D': 133.103, 'E': 147.129, 'F': 165.189, 'G': 75.067, - 'H': 155.155, 'I': 131.173, 'K': 146.188, 'L': 131.173, 'M': 149.211, 'N': 132.118, - 'P': 115.131, 'Q': 146.145, 'R': 174.20, 'S': 105.093, 'T': 119.119, 'V': 117.146, - 'W': 204.225, 'Y': 181.189} - return weights - - -def count_aas(seq, scale='relative'): - """Function to count the amino acids occuring in a given sequence. - - :param seq: {str} amino acid sequence - :param scale: {'absolute' or 'relative'} defines whether counts or frequencies are given for each AA - :return: {dict} dictionary with amino acids as keys and their counts in the sequence as values. - """ - if seq == '': # error if len(seq) == 0 - seq = ' ' - aas = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] - scl = 1. - if scale == 'relative': - scl = len(seq) - aa = {a: (float(seq.count(a)) / scl) for a in aas} - aa = collections.OrderedDict(sorted(list(aa.items()))) - return aa - - -def count_ngrams(seq, n): - """Function to count the n-grams of an amino acid sequence. N can be one integer or a list of integers - - :param seq: {str} amino acid sequence - :param n: {int or list of ints} defines whether counts or frequencies are given for each AA - :return: {dict} dictionary with n-grams as keys and their counts in the sequence as values. - """ - if seq == '': - seq = ' ' - if isinstance(n, int): - n = [n] - ngrams = list() - for i in n: - ngrams.extend([seq[j:j+i] for j in range(len(seq) - (i-1))]) - counts = {g: (seq.count(g)) for g in set(ngrams)} - counts = collections.OrderedDict(sorted(counts.items(), key=operator.itemgetter(1), reverse=True)) - return counts - - -def aa_energies(): - """Function holding free energies of transfer between cyclohexane and water for all natural amino acids. - H. G. Boman, D. Wade, I. a Boman, B. Wåhlin, R. B. Merrifield, *FEBS Lett*. **1989**, *259*, 103–106. - - :return: dictionary with amino acid letters and corresponding energies. - """ - energies = {'L': -4.92, 'I': -4.92, 'V': -4.04, 'F': -2.98, 'M': -2.35, 'W': -2.33, 'A': -1.81, 'C': -1.28, - 'G': -0.94, 'Y': 0.14, 'T': 2.57, 'S': 3.40, 'H': 4.66, 'Q': 5.54, 'K': 5.55, 'N': 6.64, 'E': 6.81, - 'D': 8.72, 'R': 14.92, 'P': 0.} - return energies - - -def ngrams_apd(): - """Function returning the most frequent 2-, 3- and 4-grams from all sequences in the `APD3 - <http://aps.unmc.edu/AP/>`_, version August 2016 with 2727 sequences. - For all 2, 3 and 4grams, all possible ngrams were generated from all sequences and the top 50 most frequent - assembled into a list. Finally, leading and tailing spaces were striped and duplicates as well as ngrams containing - spaces were removed. - - :return: numpy.array containing most frequent ngrams - """ - return np.array(['AGK', 'CKI', 'RR', 'YGGG', 'LSGL', 'RG', 'YGGY', 'PRP', 'LGGG', - 'GV', 'GT', 'GS', 'GR', 'IAG', 'GG', 'GF', 'GC', 'GGYG', 'GA', 'GL', - 'GK', 'GI', 'IPC', 'KAA', 'LAK', 'GLGG', 'GGLG', 'CKIT', 'GAGK', - 'LLSG', 'LKK', 'FLP', 'LSG', 'SCK', 'LLS', 'GETC', 'VLG', 'GKLL', - 'LLG', 'C', 'KCKI', 'G', 'VGK', 'CSC', 'TKKC', 'GCS', 'GKA', 'IGK', - 'GESC', 'KVCY', 'KKL', 'KKI', 'KKC', 'LGGL', 'GLL', 'CGE', 'GGYC', - 'GLLS', 'GLF', 'AKK', 'GKAA', 'ESCV', 'GLP', 'CGES', 'PCGE', 'FL', - 'CGET', 'GLW', 'KGAA', 'KAAL', 'GGY', 'GGG', 'IKG', 'LKG', 'GGL', - 'CK', 'GTC', 'CG', 'SKKC', 'CS', 'CR', 'KC', 'AGKA', 'KA', 'KG', - 'LKCK', 'SCKL', 'KK', 'KI', 'KN', 'KL', 'SK', 'KV', 'SL', 'SC', - 'SG', 'AAA', 'VAK', 'AAL', 'AAK', 'GGGG', 'KNVA', 'GGGL', 'GYG', - 'LG', 'LA', 'LL', 'LK', 'LS', 'LP', 'GCSC', 'TC', 'GAA', 'AA', 'VA', - 'VC', 'AG', 'VG', 'AI', 'AK', 'VL', 'AL', 'TPGC', 'IK', 'IA', 'IG', - 'YGG', 'LGK', 'CSCK', 'GYGG', 'LGG', 'KGA']) - - -def aa_formulas(): - """ - Function returning the molecular formulas of all amino acids. All amino acids are considered in the neutral form - (uncharged). - """ - formulas = {'A': {'C': 3, 'H': 7, 'N': 1, 'O': 2, 'S': 0}, - 'C': {'C': 3, 'H': 7, 'N': 1, 'O': 2, 'S': 1}, - 'D': {'C': 4, 'H': 7, 'N': 1, 'O': 4, 'S': 0}, - 'E': {'C': 5, 'H': 9, 'N': 1, 'O': 4, 'S': 0}, - 'F': {'C': 9, 'H': 11, 'N': 1, 'O': 2, 'S': 0}, - 'G': {'C': 2, 'H': 5, 'N': 1, 'O': 2, 'S': 0}, - 'H': {'C': 6, 'H': 9, 'N': 3, 'O': 2, 'S': 0}, - 'I': {'C': 6, 'H': 13, 'N': 1, 'O': 2, 'S': 0}, - 'K': {'C': 6, 'H': 14, 'N': 2, 'O': 2, 'S': 0}, - 'L': {'C': 6, 'H': 13, 'N': 1, 'O': 2, 'S': 0}, - 'M': {'C': 5, 'H': 11, 'N': 1, 'O': 2, 'S': 1}, - 'N': {'C': 4, 'H': 8, 'N': 2, 'O': 3, 'S': 0}, - 'P': {'C': 5, 'H': 9, 'N': 1, 'O': 2, 'S': 0}, - 'Q': {'C': 5, 'H': 10, 'N': 2, 'O': 3, 'S': 0}, - 'R': {'C': 6, 'H': 14, 'N': 4, 'O': 2, 'S': 0}, - 'S': {'C': 3, 'H': 7, 'N': 1, 'O': 3, 'S': 0}, - 'T': {'C': 4, 'H': 9, 'N': 1, 'O': 3, 'S': 0}, - 'V': {'C': 5, 'H': 11, 'N': 1, 'O': 2, 'S': 0}, - 'W': {'C': 11, 'H': 12, 'N': 2, 'O': 2, 'S': 0}, - 'Y': {'C': 9, 'H': 11, 'N': 1, 'O': 3, 'S': 0} - } - return formulas
--- a/cpt_helical_wheel/plotWheels/descriptors.py Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1097 +0,0 @@ -# -*- coding: utf-8 -*- -""" -.. currentmodule:: modlamp.descriptors - -.. moduleauthor:: modlab Alex Mueller ETH Zurich <alex.mueller@pharma.ethz.ch> - -This module incorporates different classes to calculate peptide descriptor values. The following classes are available: - -============================= ============================================================================ -Class Characteristics -============================= ============================================================================ -:py:class:`GlobalDescriptor` Global one-dimensional peptide descriptors calculated from the AA sequence. -:py:class:`PeptideDescriptor` AA scale based global or convoluted descriptors (auto-/cross-correlated). -============================= ============================================================================ - -.. seealso:: :class:`modlamp.core.BaseDescriptor` from which the classes in :mod:`modlamp.descriptors` inherit. -""" - -import sys - -import numpy as np -from scipy import stats -from sklearn.externals.joblib import Parallel, delayed - -from plotWheels.core import BaseDescriptor, load_scale, count_aas, aa_weights, aa_energies, aa_formulas - -__author__ = "Alex Müller, Gisela Gabernet" -__docformat__ = "restructuredtext en" - - -def _one_autocorr(seq, window, scale): - """Private function used for calculating auto-correlated descriptors for 1 given sequence, window and an AA scale. - This function is used by the :py:func:`calculate_autocorr` method of :py:class:`PeptideDescriptor`. - - :param seq: {str} amino acid sequence to calculate descriptor for - :param window: {int} correlation-window size - :param scale: {str} amino acid scale to be used to calculate descriptor - :return: {numpy.array} calculated descriptor data - """ - try: - m = list() # list of lists to store translated sequence values - for l in range(len(seq)): # translate AA sequence into values - m.append(scale[str(seq[l])]) - # auto-correlation in defined sequence window - seqdesc = list() - for dist in range(window): # for all correlation distances - for val in range(len(scale['A'])): # for all features of the descriptor scale - valsum = list() - cntr = 0. - for pos in range(len(seq)): # for every position in the sequence - if (pos + dist) < len(seq): # check if corr distance is possible at that sequence position - cntr += 1 # counter to scale sum - valsum.append(m[pos][val] * m[pos + dist][val]) - seqdesc.append(sum(valsum) / cntr) # append scaled correlation distance values - return seqdesc - except ZeroDivisionError: - print("ERROR!\nThe chosen correlation window % i is larger than the sequence %s !" % (window, seq)) - - -def _one_crosscorr(seq, window, scale): - """Private function used for calculating cross-correlated descriptors for 1 given sequence, window and an AA scale. - This function is used by the :py:func:`calculate_crosscorr` method of :py:class:`PeptideDescriptor`. - - :param seq: {str} amino acid sequence to calculate descriptor for - :param window: {int} correlation-window size - :param scale: {str} amino acid scale to be used to calculate descriptor - :return: {numpy.array} calculated descriptor data - """ - try: - m = list() # list of lists to store translated sequence values - for l in range(len(seq)): # translate AA sequence into values - m.append(scale[str(seq[l])]) - # auto-correlation in defined sequence window - seqdesc = list() - for val in range(len(scale['A'])): # for all features of the descriptor scale - for cc in range(len(scale['A'])): # for every feature cross correlation - if (val + cc) < len(scale['A']): # check if corr distance is in range of the num of features - for dist in range(window): # for all correlation distances - cntr = float() - valsum = list() - for pos in range(len(seq)): # for every position in the sequence - if (pos + dist) < len(seq): # check if corr distance is possible at that sequence pos - cntr += 1 # counter to scale sum - valsum.append(m[pos][val] * m[pos + dist][val + cc]) - seqdesc.append(sum(valsum) / cntr) # append scaled correlation distance values - return seqdesc - except ZeroDivisionError: - print("ERROR!\nThe chosen correlation window % i is larger than the sequence %s !" % (window, seq)) - - -def _one_arc(seq, modality, scale): - """ Privat function used for calculating arc descriptors for one sequence and AA scale. This function is used by - :py:func:`calculate_arc` method method of :py:class:`PeptideDescriptor`. - - :param seq: {str} amino acid sequence to calculate descriptor for - :param scale: {str} amino acid scale to be used to calculate descriptor - :return: {numpy.array} calculated descriptor data - """ - desc_mat = [] - for aa in seq: - desc_mat.append(scale[aa]) - desc_mat = np.asarray(desc_mat) - - # Check descriptor dimension - desc_dim = desc_mat.shape[1] - - # list to store descriptor values for all windows - allwindows_arc = [] - - if len(seq) > 18: - window = 18 - # calculates number of windows in sequence - num_windows = len(seq) - window - else: - window = len(seq) - num_windows = 1 - - # loop through all windows - for j in range(num_windows): - # slices descriptor matrix into current window - window_mat = desc_mat[j:j + window, :] - - # defines order of amino acids in helical projection - order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7] - - # orders window descriptor matrix into helical projection order - ordered = [] - for pos in order: - try: - ordered.append(window_mat[pos, :]) - except: - # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions - ordered.append([2] * desc_dim) - ordered = np.asarray(ordered) - - window_arc = [] - - # loop through pharmacophoric features - for m in range(desc_dim): - all_arcs = [] # stores all arcs that can be found of a pharmacophoric feature - arc = 0 - - for n in range(18): # for all positions in helix, regardless of sequence length - if ordered[n, m] == 0: # if position does not contain pharmacophoric feature - all_arcs.append(arc) # append previous arc to all arcs list - arc = 0 # arc is initialized - elif ordered[n, m] == 1: # if position contains pharmacophoric feature(PF), elongate arc by 20° - arc += 20 - elif ordered[n, m] == 2: # if position doesn't contain amino acid: - if ordered[n - 1, m] == 1: # if previous position contained PF add 10° - arc += 10 - elif ordered[n - 1, m] == 0: # if previous position didn't contain PF don't add anything - arc += 0 - elif ordered[ - n - 2, m] == 1: # if previous position is empty then check second previous for PF - arc += 10 - if n == 17: # if we are at the last position check for position n=0 instead of next position. - if ordered[0, m] == 1: # if it contains PF add 10° extra - arc += 10 - else: # if next position contains PF add 10° extra - if ordered[n + 1, m] == 1: - arc += 10 - elif ordered[n + 1, m] == 0: - arc += 0 - else: # if next position is empty check for 2nd next position - if n == 16: - if ordered[0, m] == 1: - arc += 10 - else: - if ordered[n + 2, m] == 1: - arc += 10 - - all_arcs.append(arc) - if not arc == 360: - arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together - all_arcs = [arc0] + all_arcs[1:] - - window_arc.append(np.max(all_arcs)) # append to window arcs the maximum arc of this PF - allwindows_arc.append(window_arc) # append all PF arcs of this window - - allwindows_arc = np.asarray(allwindows_arc) - - if modality == 'max': - final_arc = np.max(allwindows_arc, axis=0) # calculate maximum / mean arc along all windows - elif modality == 'mean': - final_arc = np.mean(allwindows_arc, axis=0) - else: - print('modality is unknown, please choose between "max" and "mean"\n.') - sys.exit() - return final_arc - - -def _charge(seq, ph=7.0, amide=False): - """Calculates charge of a single sequence. The method used is first described by Bjellqvist. In the case of - amidation, the value for the 'Cterm' pKa is 15 (and Cterm is added to the pos_pks dictionary. - The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). - - **pos_pks** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} - - **neg_pks** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} - - :param ph: {float} pH at which to calculate peptide charge. - :param amide: {boolean} whether the sequences have an amidated C-terminus. - :return: {array} descriptor values in the attribute :py:attr:`descriptor - """ - - if amide: - pos_pks = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} - neg_pks = {'Cterm': 15., 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} - else: - pos_pks = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} - neg_pks = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} - - aa_content = count_aas(seq, scale='absolute') - aa_content['Nterm'] = 1.0 - aa_content['Cterm'] = 1.0 - pos_charge = 0.0 - for aa, pK in pos_pks.items(): - c_r = 10 ** (pK - ph) - partial_charge = c_r / (c_r + 1.0) - pos_charge += aa_content[aa] * partial_charge - neg_charge = 0.0 - for aa, pK in neg_pks.items(): - c_r = 10 ** (ph - pK) - partial_charge = c_r / (c_r + 1.0) - neg_charge += aa_content[aa] * partial_charge - return round(pos_charge - neg_charge, 3) - - -class GlobalDescriptor(BaseDescriptor): - """ - Base class for global, non-amino acid scale dependant descriptors. The following descriptors can be calculated by - the **methods** linked below: - - - `Sequence Length <modlamp.html#modlamp.descriptors.GlobalDescriptor.length>`_ - - `Molecular Formula <modlamp.html#modlamp.descriptors.GlobalDescriptor.formula>`_ - - `Molecular Weight <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_MW>`_ - - `Sequence Charge <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_charge>`_ - - `Charge Density <modlamp.html#modlamp.descriptors.GlobalDescriptor.charge_density>`_ - - `Isoelectric Point <modlamp.html#modlamp.descriptors.GlobalDescriptor.isoelectric_point>`_ - - `Instability Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.instability_index>`_ - - `Aromaticity <modlamp.html#modlamp.descriptors.GlobalDescriptor.aromaticity>`_ - - `Aliphatic Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.aliphatic_index>`_ - - `Boman Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.boman_index>`_ - - `Hydrophobic Ratio <modlamp.html#modlamp.descriptors.GlobalDescriptor.hydrophobic_ratio>`_ - - `all of the above <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_all>`_ - """ - - def length(self, append=False): - """ - Method to calculate the length (total AA count) of every sequence in the attribute :py:attr:`sequences`. - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of sequence lengths in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor(['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR']) - >>> desc.length() - >>> desc.descriptor - array([[ 8.], [12.], [12.]]) - """ - desc = [] - for seq in self.sequences: - desc.append(float(len(seq.strip()))) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('Length') - else: - self.descriptor = np.array(desc) - self.featurenames = ['Length'] - - def formula(self, amide=False, append=False): - """Method to calculate the molecular formula of every sequence in the attribute :py:attr:`sequences`. - - :param amide: {boolean} whether the sequences are C-terminally amidated. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of molecular formulas {str} in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor(['KADSFLSADGHSADFSLDKKLKERL', 'ERTILSDFPQWWFASLDFLNC', 'ACDEFGHIKLMNPQRSTVWY']) - >>> desc.formula(amide=True) - >>> for v in desc.descriptor: - ... print(v[0]) - C122 H197 N35 O39 - C121 H168 N28 O33 S - C106 H157 N29 O30 S2 - - .. seealso:: :py:func:`modlamp.core.aa_formulas()` - - .. versionadded:: v2.7.6 - """ - desc = [] - formulas = aa_formulas() - for seq in self.sequences: - f = {'C': 0, 'H': 0, 'N': 0, 'O': 0, 'S': 0} - for aa in seq: # loop over all AAs - for k in f.keys(): - f[k] += formulas[aa][k] - - # substract H2O for every peptide bond - f['H'] -= 2 * (len(seq) - 1) - f['O'] -= (len(seq) - 1) - - if amide: # add C-terminal amide --> replace OH with NH2 - f['O'] -= 1 - f['H'] += 1 - f['N'] += 1 - - if f['S'] != 0: - val = 'C%s H%s N%s O%s %s%s' % (f['C'], f['H'], f['N'], f['O'], 'S', f['S']) - else: - val = 'C%s H%s N%s O%s' % (f['C'], f['H'], f['N'], f['O']) - - desc.append([val]) - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('Formula') - else: - self.descriptor = np.array(desc) - self.featurenames = ['Formula'] - - def calculate_MW(self, amide=False, append=False): - """Method to calculate the molecular weight [g/mol] of every sequence in the attribute :py:attr:`sequences`. - - :param amide: {boolean} whether the sequences are C-terminally amidated (subtracts 0.95 from the MW). - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('IAESFKGHIPL') - >>> desc.calculate_MW(amide=True) - >>> desc.descriptor - array([[ 1210.43]]) - - .. seealso:: :py:func:`modlamp.core.aa_weights()` - - .. versionchanged:: v2.1.5 amide option added - """ - desc = [] - weights = aa_weights() - for seq in self.sequences: - mw = [] - for aa in seq: # sum over aa weights - mw.append(weights[aa]) - desc.append(round(sum(mw) - 18.015 * (len(seq) - 1), 2)) # sum over AA MW and subtract H20 MW for every - # peptide bond - desc = np.asarray(desc).reshape(len(desc), 1) - if amide: # if sequences are amidated, subtract 0.98 from calculated MW (OH - NH2) - desc = [d - 0.98 for d in desc] - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('MW') - else: - self.descriptor = np.array(desc) - self.featurenames = ['MW'] - - def calculate_charge(self, ph=7.0, amide=False, append=False): - """Method to overall charge of every sequence in the attribute :py:attr:`sequences`. - - The method used is first described by Bjellqvist. In the case of amidation, the value for the 'Cterm' pKa is 15 - (and Cterm is added to the pos_pKs dictionary. - The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). - - **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} - - **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} - - :param ph: {float} ph at which to calculate peptide charge. - :param amide: {boolean} whether the sequences have an amidated C-terminus. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('KLAKFGKRSELVALSG') - >>> desc.calculate_charge(ph=7.4, amide=True) - >>> desc.descriptor - array([[ 3.989]]) - """ - - desc = [] - for seq in self.sequences: - desc.append(_charge(seq, ph, amide)) # calculate charge with helper function - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('Charge') - else: - self.descriptor = np.array(desc) - self.featurenames = ['Charge'] - - def charge_density(self, ph=7.0, amide=False, append=False): - """Method to calculate the charge density (charge / MW) of every sequences in the attributes :py:attr:`sequences` - - :param ph: {float} pH at which to calculate peptide charge. - :param amide: {boolean} whether the sequences have an amidated C-terminus. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor`. - :Example: - - >>> desc = GlobalDescriptor('GNSDLLIEQRTLLASDEF') - >>> desc.charge_density(ph=6, amide=True) - >>> desc.descriptor - array([[-0.00097119]]) - """ - self.calculate_charge(ph, amide) - charges = self.descriptor - self.calculate_MW(amide) - masses = self.descriptor - desc = charges / masses - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('ChargeDensity') - else: - self.descriptor = np.array(desc) - self.featurenames = ['ChargeDensity'] - - def isoelectric_point(self, amide=False, append=False): - """ - Method to calculate the isoelectric point of every sequence in the attribute :py:attr:`sequences`. - The pK scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). - - **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} - - **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} - - :param amide: {boolean} whether the sequences have an amidated C-terminus. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('KLFDIKFGHIPQRST') - >>> desc.isoelectric_point() - >>> desc.descriptor - array([[ 10.6796875]]) - """ - ph, ph1, ph2 = float(), float(), float() - desc = [] - for seq in self.sequences: - - # Bracket between ph1 and ph2 - ph = 7.0 - charge = _charge(seq, ph, amide) - if charge > 0.0: - ph1 = ph - charge1 = charge - while charge1 > 0.0: - ph = ph1 + 1.0 - charge = _charge(seq, ph, amide) - if charge > 0.0: - ph1 = ph - charge1 = charge - else: - ph2 = ph - break - else: - ph2 = ph - charge2 = charge - while charge2 < 0.0: - ph = ph2 - 1.0 - charge = _charge(seq, ph, amide) - if charge < 0.0: - ph2 = ph - charge2 = charge - else: - ph1 = ph - break - # Bisection - while ph2 - ph1 > 0.0001 and charge != 0.0: - ph = (ph1 + ph2) / 2.0 - charge = _charge(seq, ph, amide) - if charge > 0.0: - ph1 = ph - else: - ph2 = ph - desc.append(ph) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('pI') - else: - self.descriptor = np.array(desc) - self.featurenames = ['pI'] - - def instability_index(self, append=False): - """ - Method to calculate the instability of every sequence in the attribute :py:attr:`sequences`. - The instability index is a prediction of protein stability based on the amino acid composition. - ([1] K. Guruprasad, B. V Reddy, M. W. Pandit, Protein Eng. 1990, 4, 155–161.) - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('LLASMNDLLAKRST') - >>> desc.instability_index() - >>> desc.descriptor - array([[ 63.95714286]]) - """ - - desc = [] - dimv = load_scale('instability')[1] - for seq in self.sequences: - stabindex = float() - for i in range(len(seq) - 1): - stabindex += dimv[seq[i]][seq[i+1]] - desc.append((10.0 / len(seq)) * stabindex) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('InstabilityInd') - else: - self.descriptor = np.array(desc) - self.featurenames = ['InstabilityInd'] - - def aromaticity(self, append=False): - """ - Method to calculate the aromaticity of every sequence in the attribute :py:attr:`sequences`. - According to Lobry, 1994, it is simply the relative frequency of Phe+Trp+Tyr. - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('GLFYWRFFLQRRFLYWW') - >>> desc.aromaticity() - >>> desc.descriptor - array([[ 0.52941176]]) - """ - desc = [] - for seq in self.sequences: - f = seq.count('F') - w = seq.count('W') - y = seq.count('Y') - desc.append(float(f + w + y) / len(seq)) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('Aromaticity') - else: - self.descriptor = np.array(desc) - self.featurenames = ['Aromaticity'] - - def aliphatic_index(self, append=False): - """ - Method to calculate the aliphatic index of every sequence in the attribute :py:attr:`sequences`. - According to Ikai, 1980, the aliphatic index is a measure of thermal stability of proteins and is dependant - on the relative volume occupied by aliphatic amino acids (A,I,L & V). - ([1] A. Ikai, J. Biochem. 1980, 88, 1895–1898.) - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('KWLKYLKKLAKLVK') - >>> desc.aliphatic_index() - >>> desc.descriptor - array([[ 139.28571429]]) - """ - desc = [] - aa_dict = aa_weights() - for seq in self.sequences: - d = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa - d = {k: (float(d[k]) / len(seq)) * 100 for k in d.keys()} # get mole percent of all AA - desc.append(d['A'] + 2.9 * d['V'] + 3.9 * (d['I'] + d['L'])) # formula for calculating the AI (Ikai, 1980) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('AliphaticInd') - else: - self.descriptor = np.array(desc) - self.featurenames = ['AliphaticInd'] - - def boman_index(self, append=False): - """Method to calculate the boman index of every sequence in the attribute :py:attr:`sequences`. - According to Boman, 2003, the boman index is a measure for protein-protein interactions and is calculated by - summing over all amino acid free energy of transfer [kcal/mol] between water and cyclohexane,[2] followed by - dividing by sequence length. - ([1] H. G. Boman, D. Wade, I. a Boman, B. Wåhlin, R. B. Merrifield, *FEBS Lett*. **1989**, *259*, 103–106. - [2] A. Radzick, R. Wolfenden, *Biochemistry* **1988**, *27*, 1664–1670.) - - .. seealso:: :py:func:`modlamp.core.aa_energies()` - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('GLFDIVKKVVGALGSL') - >>> desc.boman_index() - >>> desc.descriptor - array([[-1.011875]]) - """ - d = aa_energies() - desc = [] - for seq in self.sequences: - val = [] - for a in seq: - val.append(d[a]) - desc.append(sum(val) / len(val)) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('BomanInd') - else: - self.descriptor = np.array(desc) - self.featurenames = ['BomanInd'] - - def hydrophobic_ratio(self, append=False): - """ - Method to calculate the hydrophobic ratio of every sequence in the attribute :py:attr:`sequences`, which is the - relative frequency of the amino acids **A,C,F,I,L,M & V**. - - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('VALLYWRTVLLAIII') - >>> desc.hydrophobic_ratio() - >>> desc.descriptor - array([[ 0.73333333]]) - """ - desc = [] - aa_dict = aa_weights() - for seq in self.sequences: - pa = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa - # formula for calculating the AI (Ikai, 1980): - desc.append((pa['A'] + pa['C'] + pa['F'] + pa['I'] + pa['L'] + pa['M'] + pa['V']) / float(len(seq))) - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - self.featurenames.append('HydrophRatio') - else: - self.descriptor = np.array(desc) - self.featurenames = ['HydrophRatio'] - - def calculate_all(self, ph=7.4, amide=True): - """Method combining all global descriptors and appending them into the feature matrix in the attribute - :py:attr:`descriptor`. - - :param ph: {float} pH at which to calculate peptide charge - :param amide: {boolean} whether the sequences have an amidated C-terminus. - :return: array of descriptor values in the attribute :py:attr:`descriptor` - :Example: - - >>> desc = GlobalDescriptor('AFGHFKLKKLFIFGHERT') - >>> desc.calculate_all(amide=True) - >>> desc.featurenames - ['Length', 'MW', 'ChargeDensity', 'pI', 'InstabilityInd', 'Aromaticity', 'AliphaticInd', 'BomanInd', 'HydRatio'] - >>> desc.descriptor - array([[ 18., 2.17559000e+03, 1.87167619e-03, 1.16757812e+01, ... 1.10555556e+00, 4.44444444e-01]]) - >>> desc.save_descriptor('/path/to/outputfile.csv') # save the descriptor data (with feature names header) - """ - - # This is a strange way of doing it. However, the append=True option excludes length and charge, no idea why! - fn = [] - self.length() # sequence length - l = self.descriptor - fn.extend(self.featurenames) - self.calculate_MW(amide=amide) # molecular weight - mw = self.descriptor - fn.extend(self.featurenames) - self.calculate_charge(ph=ph, amide=amide) # net charge - c = self.descriptor - fn.extend(self.featurenames) - self.charge_density(ph=ph, amide=amide) # charge density - cd = self.descriptor - fn.extend(self.featurenames) - self.isoelectric_point(amide=amide) # pI - pi = self.descriptor - fn.extend(self.featurenames) - self.instability_index() # instability index - si = self.descriptor - fn.extend(self.featurenames) - self.aromaticity() # global aromaticity - ar = self.descriptor - fn.extend(self.featurenames) - self.aliphatic_index() # aliphatic index - ai = self.descriptor - fn.extend(self.featurenames) - self.boman_index() # Boman index - bi = self.descriptor - fn.extend(self.featurenames) - self.hydrophobic_ratio() # Hydrophobic ratio - hr = self.descriptor - fn.extend(self.featurenames) - - self.descriptor = np.concatenate((l, mw, c, cd, pi, si, ar, ai, bi, hr), axis=1) - self.featurenames = fn - - -class PeptideDescriptor(BaseDescriptor): - """Base class for peptide descriptors. The following **amino acid descriptor scales** are available for descriptor - calculation: - - - **AASI** (An amino acid selectivity index scale for helical antimicrobial peptides, *[1] D. Juretić, D. Vukicević, N. Ilić, N. Antcheva, A. Tossi, J. Chem. Inf. Model. 2009, 49, 2873–2882.*) - - **ABHPRK** (modlabs inhouse physicochemical feature scale (Acidic, Basic, Hydrophobic, Polar, aRomatic, Kink-inducer) - - **argos** (Argos hydrophobicity amino acid scale, *[2] Argos, P., Rao, J. K. M. & Hargrave, P. A., Eur. J. Biochem. 2005, 128, 565–575.*) - - **bulkiness** (Amino acid side chain bulkiness scale, *[3] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*) - - **charge_phys** (Amino acid charge at pH 7.0 - Hystidine charge +0.1.) - - **charge_acid** (Amino acid charge at acidic pH - Hystidine charge +1.0.) - - **cougar** (modlabs inhouse selection of global peptide descriptors) - - **eisenberg** (the Eisenberg hydrophobicity consensus amino acid scale, *[4] D. Eisenberg, R. M. Weiss, T. C. Terwilliger, W. Wilcox, Faraday Symp. Chem. Soc. 1982, 17, 109.*) - - **Ez** (potential that assesses energies of insertion of amino acid side chains into lipid bilayers, *[5] A. Senes, D. C. Chadi, P. B. Law, R. F. S. Walters, V. Nanda, W. F. DeGrado, J. Mol. Biol. 2007, 366, 436–448.*) - - **flexibility** (amino acid side chain flexibilitiy scale, *[6] R. Bhaskaran, P. K. Ponnuswamy, Int. J. Pept. Protein Res. 1988, 32, 241–255.*) - - **grantham** (amino acid side chain composition, polarity and molecular volume, *[8] Grantham, R. Science. 185, 862–864 (1974).*) - - **gravy** (GRAVY hydrophobicity amino acid scale, *[9] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*) - - **hopp-woods** (Hopp-Woods amino acid hydrophobicity scale,*[10] T. P. Hopp, K. R. Woods, Proc. Natl. Acad. Sci. 1981, 78, 3824–3828.*) - - **ISAECI** (Isotropic Surface Area (ISA) and Electronic Charge Index (ECI) of amino acid side chains, *[11] E. R. Collantes, W. J. Dunn, J. Med. Chem. 1995, 38, 2705–2713.*) - - **janin** (Janin hydrophobicity amino acid scale, *[12] J. L. Cornette, K. B. Cease, H. Margalit, J. L. Spouge, J. A. Berzofsky, C. DeLisi, J. Mol. Biol. 1987, 195, 659–685.*) - - **kytedoolittle** (Kyte & Doolittle hydrophobicity amino acid scale, *[13] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*) - - **levitt_alpha** (Levitt amino acid alpha-helix propensity scale, extracted from http://web.expasy.org/protscale. *[14] M. Levitt, Biochemistry 1978, 17, 4277-4285.*) - - **MSS** (A graph-theoretical index that reflects topological shape and size of amino acid side chains, *[15] C. Raychaudhury, A. Banerjee, P. Bag, S. Roy, J. Chem. Inf. Comput. Sci. 1999, 39, 248–254.*) - - **MSW** (Amino acid scale based on a PCA of the molecular surface based WHIM descriptor (MS-WHIM), extended to natural amino acids, *[16] A. Zaliani, E. Gancia, J. Chem. Inf. Comput. Sci 1999, 39, 525–533.*) - - **pepArc** (modlabs pharmacophoric feature scale, dimensions are: hydrophobicity, polarity, positive charge, negative charge, proline.) - - **pepcats** (modlabs pharmacophoric feature based PEPCATS scale, *[17] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*) - - **polarity** (Amino acid polarity scale, *[18] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*) - - **PPCALI** (modlabs inhouse scale derived from a PCA of 143 amino acid property scales, *[19] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*) - - **refractivity** (Relative amino acid refractivity values, *[20] T. L. McMeekin, M. Wilensky, M. L. Groves, Biochem. Biophys. Res. Commun. 1962, 7, 151–156.*) - - **t_scale** (A PCA derived scale based on amino acid side chain properties calculated with 6 different probes of the GRID program, *[21] M. Cocchi, E. Johansson, Quant. Struct. Act. Relationships 1993, 12, 1–8.*) - - **TM_tend** (Amino acid transmembrane propensity scale, extracted from http://web.expasy.org/protscale, *[22] Zhao, G., London E. Protein Sci. 2006, 15, 1987-2001.*) - - **z3** (The original three dimensional Z-scale, *[23] S. Hellberg, M. Sjöström, B. Skagerberg, S. Wold, J. Med. Chem. 1987, 30, 1126–1135.*) - - **z5** (The extended five dimensional Z-scale, *[24] M. Sandberg, L. Eriksson, J. Jonsson, M. Sjöström, S. Wold, J. Med. Chem. 1998, 41, 2481–2491.*) - - Further, amino acid scale independent methods can be calculated with help of the :class:`GlobalDescriptor` class. - - """ - - def __init__(self, seqs, scalename='Eisenberg'): - """ - :param seqs: a .fasta file with sequences, a list of sequences or a single sequence as string to calculate the - descriptor values for. - :param scalename: {str} name of the amino acid scale (one of the given list above) used to calculate the - descriptor values - :return: initialized attributes :py:attr:`sequences`, :py:attr:`names` and dictionary :py:attr:`scale` with - amino acid scale values of the scale name in :py:attr:`scalename`. - :Example: - - >>> AMP = PeptideDescriptor('KLLKLLKKLLKLLK','pepcats') - >>> AMP.sequences - ['KLLKLLKKLLKLLK'] - >>> seqs = PeptideDescriptor('/Path/to/file.fasta', 'eisenberg') # load sequences from .fasta file - >>> seqs.sequences - ['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'...] - """ - super(PeptideDescriptor, self).__init__(seqs) - self.scalename, self.scale = load_scale(scalename.lower()) - self.all_moms = list() # for passing hydrophobic moments to calculate_profile - self.all_globs = list() # for passing global to calculate_profile - - def load_scale(self, scalename): - """Method to load amino acid values from a given scale - - :param scalename: {str} name of the amino acid scale to be loaded. - :return: loaded amino acid scale values in a dictionary in the attribute :py:attr:`scale`. - - .. seealso:: :func:`modlamp.core.load_scale()` - """ - self.scalename, self.scale = load_scale(scalename.lower()) - - def calculate_autocorr(self, window, append=False): - """Method for auto-correlating the amino acid values for a given descriptor scale - - :param window: {int} correlation window for descriptor calculation in a sliding window approach - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`. - :Example: - - >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','PPCALI') - >>> AMP.calculate_autocorr(7) - >>> AMP.descriptor - array([[ 1.28442339e+00, 1.29025116e+00, 1.03240901e+00, .... ]]) - >>> AMP.descriptor.shape - (1, 133) - - .. versionchanged:: v.2.3.0 - """ - desc = Parallel(n_jobs=-1)(delayed(_one_autocorr)(seq, window, self.scale) for seq in self.sequences) - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - def calculate_crosscorr(self, window, append=False): - """Method for cross-correlating the amino acid values for a given descriptor scale - - :param window: {int} correlation window for descriptor calculation in a sliding window approach - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`. - :Example: - - >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','pepcats') - >>> AMP.calculate_crosscorr(7) - >>> AMP.descriptor - array([[ 0.6875 , 0.46666667, 0.42857143, 0.61538462, 0.58333333, ... ]]) - >>> AMP.descriptor.shape - (1, 147) - """ - desc = Parallel(n_jobs=-1)(delayed(_one_crosscorr)(seq, window, self.scale) for seq in self.sequences) - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - def calculate_moment(self, window=1000, angle=100, modality='max', append=False): - """Method for calculating the maximum or mean moment of the amino acid values for a given descriptor scale and - window. - - :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the - window, the length of the sequence is taken. So if the default window of 1000 is chosen, for all sequences - shorter than 1000, the **global** hydrophobic moment will be calculated. Otherwise, the maximal - hydrophiobic moment for the chosen window size found in the sequence will be returned. - :param angle: {int} angle in which to calculate the moment. **100** for alpha helices, **180** for beta sheets. - :param modality: {'all', 'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment. If all, - moments for all windows are returned. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global - values in :py:attr:`all_moms` (needed for the :py:func:`calculate_profile` method) - :Example: - - >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL', 'eisenberg') - >>> AMP.calculate_moment() - >>> AMP.descriptor - array([[ 0.48790226]]) - """ - if self.scale['A'] == list: - print('\n Descriptor moment calculation is only possible for one dimensional descriptors.\n') - - else: - desc = [] - for seq in self.sequences: - wdw = min(window, len(seq)) # if sequence is shorter than window, take the whole sequence instead - mtrx = [] - mwdw = [] - - for aa in range(len(seq)): - mtrx.append(self.scale[str(seq[aa])]) - - for i in range(len(mtrx) - wdw + 1): - mwdw.append(sum(mtrx[i:i + wdw], [])) - - mwdw = np.asarray(mwdw) - rads = angle * (np.pi / 180) * np.asarray(range(wdw)) # calculate actual moment (radial) - vcos = (mwdw * np.cos(rads)).sum(axis=1) - vsin = (mwdw * np.sin(rads)).sum(axis=1) - moms = np.sqrt(vsin ** 2 + vcos ** 2) / wdw - - if modality == 'max': # take window with maximal value - moment = np.max(moms) - elif modality == 'mean': # take average value over all windows - moment = np.mean(moms) - elif modality == 'all': - moment = moms - else: - print('\nERROR!\nModality parameter is wrong, please choose between "all", "max" and "mean".\n') - return - desc.append(moment) - self.all_moms.append(moms) - - desc = np.asarray(desc).reshape(len(desc), 1) # final descriptor array - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - def calculate_global(self, window=1000, modality='max', append=False): - """Method for calculating a global / window averaging descriptor value of a given AA scale - - :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the - window, the length of the sequence is taken. - :param modality: {'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment. - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global - values in :py:attr:`all_globs` (needed for the :py:func:`calculate_profile` method) - :Example: - - >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','eisenberg') - >>> AMP.calculate_global(window=1000, modality='max') - >>> AMP.descriptor - array([[ 0.44875]]) - """ - desc = list() - for n, seq in enumerate(self.sequences): - wdw = min(window, len(seq)) # if sequence is shorter than window, take the whole sequence instead - mtrx = [] - mwdw = [] - - for l in range(len(seq)): # translate AA sequence into values - mtrx.append(self.scale[str(seq[l])]) - - for i in range(len(mtrx) - wdw + 1): - mwdw.append(sum(mtrx[i:i + wdw], [])) # list of all the values for the different windows - - mwdw = np.asarray(mwdw) - glob = np.sum(mwdw, axis=1) / float(wdw) - outglob = float() - - if modality in ['max', 'mean']: - if modality == 'max': - outglob = np.max(glob) # returned moment will be the maximum of all windows - elif modality == 'mean': - outglob = np.mean(glob) # returned moment will be the mean of all windows - else: - print('Modality parameter is wrong, please choose between "max" and "mean"\n.') - return - desc.append(outglob) - self.all_globs.append(glob) - - desc = np.asarray(desc).reshape(len(desc), 1) - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - def calculate_profile(self, prof_type='uH', window=7, append=False): - """Method for calculating hydrophobicity or hydrophobic moment profiles for given sequences and fitting for - slope and intercept. The hydrophobicity scale used is "eisenberg" - - :param prof_type: prof_type of profile, available: 'H' for hydrophobicity or 'uH' for hydrophobic moment - :param window: {int} size of sliding window used (odd-numbered). - :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the - attribute :py:attr:`descriptor`. - :return: Fitted slope and intercept of calculated profile for every given sequence in the attribute - :py:attr:`descriptor`. - :Example: - - >>> AMP = PeptideDescriptor('KLLKLLKKVVGALG','kytedoolittle') - >>> AMP.calculate_profile(prof_type='H') - >>> AMP.descriptor - array([[ 0.03731293, 0.19246599]]) - """ - if prof_type == 'uH': - self.calculate_moment(window=window) - y_vals = self.all_moms - elif prof_type == 'H': - self.calculate_global(window=window) - y_vals = self.all_globs - else: - print('prof_type parameter is unknown, choose "uH" for hydrophobic moment or "H" for hydrophobicity\n.') - sys.exit() - - desc = list() - for n, seq in enumerate(self.sequences): - x_vals = range(len(seq))[int((window - 1) / 2):-int((window - 1) / 2)] - if len(seq) <= window: - slope, intercept, r_value, p_value, std_err = [0, 0, 0, 0, 0] - else: - slope, intercept, r_value, p_value, std_err = stats.linregress(x_vals, y_vals[n]) - desc.append([slope, intercept]) - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - def calculate_arc(self, modality="max", append=False): - """ Method for calculating property arcs as seen in the helical wheel plot. Use for binary amino acid scales only. - - :param modality: modality of the arc to calculate, to choose between "max" and "mean". - :param append: if true, append to current descriptor stored in the descriptor attribute. - :return: calculated descriptor as numpy.array in the descriptor attribute. - - :Example: - - >>> arc = PeptideDescriptor("KLLKLLKKLLKLLK", scalename="peparc") - >>> arc.calculate_arc(modality="max", append=False) - >>> arc.descriptor - array([[200, 160, 160, 0, 0]]) - """ - desc = Parallel(n_jobs=-1)(delayed(_one_arc)(seq, modality, self.scale) for seq in self.sequences) - - # Converts each of the amino acids to descriptor vector - for seq in self.sequences: - - # desc_mat = [] - # for aa in seq: - # desc_mat.append(self.scale[aa]) - # desc_mat = np.asarray(desc_mat) - # - # # Check descriptor dimension - # desc_dim = desc_mat.shape[1] - # - # # list to store descriptor values for all windows - # allwindows_arc = [] - # - # if len(seq) > 18: - # window = 18 - # # calculates number of windows in sequence - # num_windows = len(seq) - window - # else: - # window = len(seq) - # num_windows = 1 - # - # # loop through all windows - # for j in range(num_windows): - # # slices descriptor matrix into current window - # window_mat = desc_mat[j:j + window, :] - # - # # defines order of amino acids in helical projection - # order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7] - # - # # orders window descriptor matrix into helical projection order - # ordered = [] - # for pos in order: - # try: - # ordered.append(window_mat[pos, :]) - # except: - # # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions - # ordered.append([2] * desc_dim) - # ordered = np.asarray(ordered) - # - # window_arc = [] - # - # # loop through pharmacophoric features - # for m in range(desc_dim): - # all_arcs = [] # stores all arcs that can be found of a pharmacophoric feature - # arc = 0 - # - # for n in range(18): # for all positions in helix, regardless of sequence length - # if ordered[n, m] == 0: # if position does not contain pharmacophoric feature - # all_arcs.append(arc) # append previous arc to all arcs list - # arc = 0 # arc is initialized - # elif ordered[n, m] == 1: # if position contains pharmacophoric feature(PF), elongate arc by 20° - # arc += 20 - # elif ordered[n, m] == 2: # if position doesn't contain amino acid: - # if ordered[n - 1, m] == 1: # if previous position contained PF add 10° - # arc += 10 - # elif ordered[n - 1, m] == 0: # if previous position didn't contain PF don't add anything - # arc += 0 - # elif ordered[ - # n - 2, m] == 1: # if previous position is empty then check second previous for PF - # arc += 10 - # if n == 17: # if we are at the last position check for position n=0 instead of next position. - # if ordered[0, m] == 1: # if it contains PF add 10° extra - # arc += 10 - # else: # if next position contains PF add 10° extra - # if ordered[n + 1, m] == 1: - # arc += 10 - # elif ordered[n + 1, m] == 0: - # arc += 0 - # else: # if next position is empty check for 2nd next position - # if n == 16: - # if ordered[0, m] == 1: - # arc += 10 - # else: - # if ordered[n + 2, m] == 1: - # arc += 10 - # - # all_arcs.append(arc) - # if not arc == 360: - # arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together - # all_arcs = [arc0] + all_arcs[1:] - # - # window_arc.append(np.max(all_arcs)) # append to window arcs the maximum arc of this PF - # allwindows_arc.append(window_arc) # append all PF arcs of this window - # - # allwindows_arc = np.asarray(allwindows_arc) - # - # if modality == 'max': - # final_arc = np.max(allwindows_arc, axis=0) # calculate maximum / mean arc along all windows - # elif modality == 'mean': - # final_arc = np.mean(allwindows_arc, axis=0) - # else: - # print('modality is unknown, please choose between "max" and "mean"\n.') - # sys.exit() - - if append: - self.descriptor = np.hstack((self.descriptor, np.array(desc))) - else: - self.descriptor = np.array(desc) - - - - - - - -
--- a/cpt_helical_wheel/plotWheels/helical_wheel.py Tue Jul 05 05:21:34 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,233 +0,0 @@ -import matplotlib -matplotlib.use('Agg') - -import matplotlib.lines as lines -import matplotlib.patches as patches -import matplotlib.pyplot as plt -#from mpl_toolkits.mplot3d import Axes3D -import numpy as np -from scipy.stats.kde import gaussian_kde - -from plotWheels.core import load_scale -from plotWheels.descriptors import PeptideDescriptor - -def helical_wheel(sequence, colorcoding='rainbow', text_color=None, - lineweights=True, filename=None, seq=False, moment=False, - seqRange=1, t_size=32, rot=float(90), dpi=150, numbering=True): - """A function to project a given peptide sequence onto a helical wheel plot. It can be useful to illustrate the - properties of alpha-helices, like positioning of charged and hydrophobic residues along the sequence. - - :param sequence: {str} the peptide sequence for which the helical wheel should be drawn - :param colorcoding: {str} the color coding to be used, available: *rainbow*, *charge*, *polar*, *simple*, - *amphipathic*, *custom_input*, *none* - :param lineweights: {boolean} defines whether connection lines decrease in thickness along the sequence - :param filename: {str} filename where to save the plot. *default = None* --> show the plot - :param seq: {bool} whether the amino acid sequence should be plotted as a title - :param moment: {bool} whether the Eisenberg hydrophobic moment should be calculated and plotted - :param seqRange: {int} starting value of residue location in sequence - :param t_size: {int} text size - :param rot: {float} rotation by radians --> converted to degrees. - :param dpi: {int} dpi parameter for saved files - :return: a helical wheel projection plot of the given sequence (interactively or in **filename**) - :Example: - - >>> helical_wheel('GLFDIVKKVVGALG') - >>> helical_wheel('KLLKLLKKLLKLLK', colorcoding='charge') - >>> helical_wheel('AKLWLKAGRGFGRG', colorcoding='none', lineweights=False) - >>> helical_wheel('ACDEFGHIKLMNPQRSTVWY') - - .. image:: ../docs/static/wheel1.png - :height: 300px - .. image:: ../docs/static/wheel2.png - :height: 300px - .. image:: ../docs/static/wheel3.png - :height: 300px - .. image:: ../docs/static/wheel4.png - :height: 300px - - .. versionadded:: v2.1.5 - """ - # color mappings - aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] - if colorcoding == type(str): - f_rainbow = ['#3e3e28', '#ffcc33', '#b30047', '#b30047', '#ffcc33', '#3e3e28', '#80d4ff', '#ffcc33', '#0047b3', - '#ffcc33', '#ffcc33', '#b366ff', '#29a329', '#b366ff', '#0047b3', '#ff66cc', '#ff66cc', '#ffcc33', - '#ffcc33', '#ffcc33'] - f_charge = ['#000000', '#000000', '#ff4d94', '#ff4d94', '#000000', '#000000', '#80d4ff', '#000000', '#80d4ff', - '#000000', '#000000', '#000000', '#000000', '#000000', '#80d4ff', '#000000', '#000000', '#000000', - '#000000', '#000000'] - f_polar = ['#000000', '#000000', '#80d4ff', '#80d4ff', '#000000', '#000000', '#80d4ff', '#000000', '#80d4ff', - '#000000', '#000000', '#80d4ff', '#000000', '#80d4ff', '#80d4ff', '#80d4ff', '#80d4ff', '#000000', - '#000000', '#000000'] - f_simple = ['#ffcc33', '#ffcc33', '#0047b3', '#0047b3', '#ffcc33', '#7f7f7f', '#0047b3', '#ffcc33', '#0047b3', - '#ffcc33', '#ffcc33', '#0047b3', '#ffcc33', '#0047b3', '#0047b3', '#0047b3', '#0047b3', '#ffcc33', - '#ffcc33', '#ffcc33'] - f_none = ['#ffffff'] * 20 - f_amphi = ['#ffcc33', '#29a329', '#b30047', '#b30047', '#f79318', '#80d4ff', '#0047b3', '#ffcc33', '#0047b3', - '#ffcc33', '#ffcc33', '#80d4ff', '#29a329', '#80d4ff', '#0047b3', '#80d4ff', '#80d4ff', '#ffcc33', - '#f79318', '#f79318'] - t_rainbow = ['w', 'k', 'w', 'w', 'k', 'w', 'k', 'k', 'w', 'k', 'k', 'k', 'k', 'k', 'w', 'k', 'k', 'k', 'k', 'k'] - t_charge = ['w', 'w', 'k', 'k', 'w', 'w', 'k', 'w', 'k', 'w', 'w', 'w', 'w', 'w', 'k', 'w', 'w', 'w', 'w', 'w'] - t_polar = ['w', 'w', 'k', 'k', 'w', 'w', 'k', 'w', 'k', 'w', 'w', 'k', 'w', 'k', 'k', 'k', 'k', 'w', 'w', 'w'] - t_simple = ['k', 'k', 'w', 'w', 'k', 'w', 'w', 'k', 'w', 'k', 'k', 'k', 'k', 'w', 'w', 'w', 'w', 'k', 'k', 'k'] - t_none = ['k'] * 20 - t_amphi = ['k', 'k', 'w', 'w', 'w', 'k', 'w', 'k', 'w', 'k', 'k', 'k', 'w', 'k', 'w', 'k', 'k', 'k', 'w', 'w'] - d_eisberg = load_scale('eisenberg')[1] # eisenberg hydrophobicity values for HM - else: - f_custom = colorcoding - t_custom = text_color - d_eisberg = load_scale('eisenberg')[1] - - if lineweights: - lw = np.arange(0.1, 5.5, 5. / (len(sequence) - 1)) # line thickness array - lw = lw[::-1] # inverse order - else: - lw = [2.] * (len(sequence) - 1) - # check which color coding to use - if colorcoding == type(str): - if colorcoding == 'rainbow': - df = dict(zip(aa, f_rainbow)) - dt = dict(zip(aa, t_rainbow)) - elif colorcoding == 'charge': - df = dict(zip(aa, f_charge)) - dt = dict(zip(aa, t_charge)) - elif colorcoding == 'polar': - df = dict(zip(aa, f_polar)) - dt = dict(zip(aa, t_polar)) - elif colorcoding == 'simple': - df = dict(zip(aa, f_simple)) - dt = dict(zip(aa, t_simple)) - elif colorcoding == 'none': - df = dict(zip(aa, f_none)) - dt = dict(zip(aa, t_none)) - elif colorcoding == 'amphipathic': - df = dict(zip(aa, f_amphi)) - dt = dict(zip(aa, t_amphi)) - else: - print("Unknown color coding, 'rainbow' used instead") - df = dict(zip(aa, f_rainbow)) - dt = dict(zip(aa, t_rainbow)) - else: - df = dict(zip(aa, f_custom)) - dt = dict(zip(aa, t_custom)) - - # degree to radian - deg = np.arange(float(len(sequence))) * -100. - deg = [d + rot for d in deg] # start at 270 degree in unit circle (on top) - rad = np.radians(deg) - - # dict for coordinates and eisenberg values - d_hydro = dict(zip(rad, [0.] * len(rad))) - - # create figure - fig = plt.figure(frameon=False, figsize=(10, 10)) - ax = fig.add_subplot(111) - old = None - hm = list() - - # iterate over sequence - for i, r in enumerate(rad): - new = (np.cos(r), np.sin(r)) # new AA coordinates - if i < 18: - # plot the connecting lines - if old is not None: - line = lines.Line2D((old[0], new[0]), (old[1], new[1]), transform=ax.transData, color='k', - linewidth=lw[i - 1]) - line.set_zorder(1) # 1 = level behind circles - ax.add_line(line) - elif 17 < i < 36: - line = lines.Line2D((old[0], new[0]), (old[1], new[1]), transform=ax.transData, color='k', - linewidth=lw[i - 1]) - line.set_zorder(1) # 1 = level behind circles - ax.add_line(line) - new = (np.cos(r) * 1.2, np.sin(r) * 1.2) - elif i == 36: - line = lines.Line2D((old[0], new[0]), (old[1], new[1]), transform=ax.transData, color='k', - linewidth=lw[i - 1]) - line.set_zorder(1) # 1 = level behind circles - ax.add_line(line) - new = (np.cos(r) * 1.4, np.sin(r) * 1.4) - else: - new = (np.cos(r) * 1.4, np.sin(r) * 1.4) - - # plot circles - circ = patches.Circle(new, radius=0.125, transform=ax.transData, edgecolor='k', facecolor=df[sequence[i]]) - circ.set_zorder(2) # level in front of lines - ax.add_patch(circ) - - # check if N- or C-terminus and add subscript, then plot AA letter - if numbering: - size = t_size - if i == 0: - ax.text(new[0], new[1], sequence[i] + '$_N$', va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - elif i == len(sequence) - 1: - ax.text(new[0], new[1], sequence[i] + '$_C$', va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - else: - seqRange += 1 - ax.text(new[0], new[1], sequence[i] + '$_{'+str(seqRange)+'}$', va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - - eb = d_eisberg[sequence[i]][0] # eisenberg value for this AA - hm.append([eb * new[0], eb * new[1]]) # save eisenberg hydrophobicity vector value to later calculate HM - - old = (np.cos(r), np.sin(r)) # save as previous coordinates - - else: - size = t_size - if i == 0: - ax.text(new[0], new[1], sequence[i] + '$_N$', va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - elif i == len(sequence) - 1: - ax.text(new[0], new[1], sequence[i] + '$_C$', va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - else: - ax.text(new[0], new[1], sequence[i], va='center', ha='center', transform=ax.transData, - size=size, color=dt[sequence[i]], fontweight='bold') - - eb = d_eisberg[sequence[i]][0] # eisenberg value for this AA - hm.append([eb * new[0], eb * new[1]]) # save eisenberg hydrophobicity vector value to later calculate HM - - old = (np.cos(r), np.sin(r)) # save as previous coordinates - - # draw hydrophobic moment arrow if moment option - if moment: - v_hm = np.sum(np.array(hm), 0) - x = .0333 * v_hm[0] - y = .0333 * v_hm[1] - ax.arrow(0., 0., x, y, head_width=0.04, head_length=0.03, transform=ax.transData, - color='k', linewidth=6.) - desc = PeptideDescriptor(sequence) # calculate hydrophobic moment - desc.calculate_moment() - if abs(x) < 0.2 and y > 0.: # right positioning of HM text so arrow does not cover it - z = -0.2 - else: - z = 0.2 - plt.text(0., z, str(round(desc.descriptor[0][0], 3)), fontdict={'fontsize': 20, 'fontweight': 'bold', - 'ha': 'center'}) - - # plot shape - if len(sequence) < 19: - ax.set_xlim(-1.2, 1.2) - ax.set_ylim(-1.2, 1.2) - else: - ax.set_xlim(-1.4, 1.4) - ax.set_ylim(-1.4, 1.4) - ax.spines['right'].set_visible(False) - ax.spines['top'].set_visible(False) - ax.spines['left'].set_visible(False) - ax.spines['bottom'].set_visible(False) - cur_axes = plt.gca() - cur_axes.axes.get_xaxis().set_visible(False) - cur_axes.axes.get_yaxis().set_visible(False) - plt.tight_layout() - - if seq: - plt.title(sequence, fontweight='bold', fontsize=20) - - # show or save plot - if filename: - plt.savefig(filename, dpi=dpi) - else: - plt.show()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/generateHelicalWheel.py Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,131 @@ +## + +import argparse +from plotWheels.helical_wheel import helical_wheel + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Generate Helical Wheel") + parser.add_argument("--sequence", dest="sequence", type=str) + parser.add_argument("--seqRange", dest="seqRange", type=int, default=1) + parser.add_argument("--t_size", dest="t_size", type=int, default=32) + parser.add_argument("--rotation", dest="rotation", type=int, default=90) + parser.add_argument( + "--numbering", action="store_true", help="numbering for helical wheel" + ) + parser.add_argument( + "--output", + dest="output", + type=argparse.FileType("wb"), + default="_helicalwheel.png", + ) # dest="output",default="_helicalwheel.png") + #### circle colors + parser.add_argument("--f_A", dest="f_A", default="#ffcc33") + parser.add_argument("--f_C", dest="f_C", default="#b5b5b5") + parser.add_argument("--f_D", dest="f_D", default="#db270f") + parser.add_argument("--f_E", dest="f_E", default="#db270f") + parser.add_argument("--f_F", dest="f_F", default="#ffcc33") + parser.add_argument("--f_G", dest="f_G", default="#b5b5b5") + parser.add_argument("--f_H", dest="f_H", default="#12d5fc") + parser.add_argument("--f_I", dest="f_I", default="#ffcc33") + parser.add_argument("--f_K", dest="f_K", default="#12d5fc") + parser.add_argument("--f_L", dest="f_L", default="#ffcc33") + parser.add_argument("--f_M", dest="f_M", default="#ffcc33") + parser.add_argument("--f_N", dest="f_N", default="#b5b5b5") + parser.add_argument("--f_P", dest="f_P", default="#ffcc33") + parser.add_argument("--f_Q", dest="f_Q", default="#b5b5b5") + parser.add_argument("--f_R", dest="f_R", default="#12d5fc") + parser.add_argument("--f_S", dest="f_S", default="#b5b5b5") + parser.add_argument("--f_T", dest="f_T", default="#b5b5b5") + parser.add_argument("--f_V", dest="f_V", default="#ffcc33") + parser.add_argument("--f_W", dest="f_W", default="#ffcc33") + parser.add_argument("--f_Y", dest="f_Y", default="#b5b5b5") + ### text colors + parser.add_argument("--t_A", dest="t_A", default="k") + parser.add_argument("--t_C", dest="t_C", default="k") + parser.add_argument("--t_D", dest="t_D", default="w") + parser.add_argument("--t_E", dest="t_E", default="w") + parser.add_argument("--t_F", dest="t_F", default="k") + parser.add_argument("--t_G", dest="t_G", default="k") + parser.add_argument("--t_H", dest="t_H", default="k") + parser.add_argument("--t_I", dest="t_I", default="k") + parser.add_argument("--t_K", dest="t_K", default="k") + parser.add_argument("--t_L", dest="t_L", default="k") + parser.add_argument("--t_M", dest="t_M", default="k") + parser.add_argument("--t_N", dest="t_N", default="k") + parser.add_argument("--t_P", dest="t_P", default="k") + parser.add_argument("--t_Q", dest="t_Q", default="k") + parser.add_argument("--t_R", dest="t_R", default="k") + parser.add_argument("--t_S", dest="t_S", default="k") + parser.add_argument("--t_T", dest="t_T", default="k") + parser.add_argument("--t_V", dest="t_V", default="k") + parser.add_argument("--t_W", dest="t_W", default="k") + parser.add_argument("--t_Y", dest="t_Y", default="k") + + args = parser.parse_args() + + # print(type(args.output)) + + f_colors = [ + args.f_A, + args.f_C, + args.f_D, + args.f_E, + args.f_F, + args.f_G, + args.f_H, + args.f_I, + args.f_K, + args.f_L, + args.f_M, + args.f_N, + args.f_P, + args.f_Q, + args.f_R, + args.f_S, + args.f_T, + args.f_V, + args.f_W, + args.f_Y, + ] + + t_colors = [ + args.t_A, + args.t_C, + args.t_D, + args.t_E, + args.t_F, + args.t_G, + args.t_H, + args.t_I, + args.t_K, + args.t_L, + args.t_M, + args.t_N, + args.t_P, + args.t_Q, + args.t_R, + args.t_S, + args.t_T, + args.t_V, + args.t_W, + args.t_Y, + ] + + colors = [f_colors, t_colors] + + tmp_file = "./tmp.png" + + helical_wheel( + sequence=args.sequence, + colorcoding=colors[0], + text_color=colors[1], + seqRange=args.seqRange, + t_size=args.t_size, + rot=args.rotation, + numbering=args.numbering, + filename=tmp_file, + ) + + with open("tmp.png", "rb") as f: + for line in f: + args.output.write(line)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/generateHelicalWheel.xml Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,387 @@ +<tool id="edu.tamu.cpt2.helicalWheel.generateHelicalWheel" name="Helical Wheel" version="1.0"> + <description>Generate and Plot a Protein Helical Wheel</description> + <macros> + <import>cpt-macros.xml</import> + <import>macros.xml</import> + </macros> + <expand macro="requirements"> + <requirement type="package">numpy</requirement> + <requirement type="package">pandas</requirement> + <requirement type="package" version="0.18.1">scikit-learn</requirement> + <requirement type="package">scipy</requirement> + <requirement type="package">matplotlib</requirement> + </expand> + <command detect_errors="aggressive"><![CDATA[ +python '$__tool_directory__/generateHelicalWheel.py' +--sequence '$sequence' +--seqRange '$seqRange' +--t_size '$t_size' +--rotation '$rotation' +'$numbering' +--f_A "$sec_B.f_A" +--f_C "$sec_C.f_C" +--f_D "$sec_D.f_D" +--f_E "$sec_D.f_E" +--f_F "$sec_B.f_F" +--f_G "$sec_C.f_G" +--f_H "$sec_E.f_H" +--f_I "$sec_B.f_I" +--f_K "$sec_E.f_K" +--f_L "$sec_B.f_L" +--f_M "$sec_B.f_M" +--f_N "$sec_C.f_N" +--f_P "$sec_B.f_P" +--f_Q "$sec_C.f_Q" +--f_R "$sec_E.f_R" +--f_S "$sec_C.f_S" +--f_T "$sec_C.f_T" +--f_V "$sec_B.f_V" +--f_W "$sec_B.f_W" +--f_Y "$sec_C.f_Y" +--t_A "$sec_B.t_A" +--t_C "$sec_C.t_C" +--t_D "$sec_D.t_D" +--t_E "$sec_D.t_E" +--t_F "$sec_B.t_F" +--t_G "$sec_C.t_G" +--t_H "$sec_E.t_H" +--t_I "$sec_B.t_I" +--t_K "$sec_E.t_K" +--t_L "$sec_B.t_L" +--t_M "$sec_B.t_M" +--t_N "$sec_C.t_N" +--t_P "$sec_B.t_P" +--t_Q "$sec_C.t_Q" +--t_R "$sec_E.t_R" +--t_S "$sec_C.t_S" +--t_T "$sec_C.t_T" +--t_V "$sec_B.t_V" +--t_W "$sec_B.t_W" +--t_Y "$sec_C.t_Y" +--output '$output' +]]></command> + <inputs> + <param label="Paste in exact sequence to be plotted" name="sequence" type="text"/> + <param label="Label Start Number" name="seqRange" type="integer" value="1" help="starting residue number to use for labels"/> + <param label="Amino Acid Text Size" name="t_size" type="integer" value="32" help="Alters the Text Size. Default is 32"/> + <param label="Rotation" name="rotation" type="integer" value="90" help="Rotates the helical wheel. Default is 90"/> + <param label="Label Numbering Text" name="numbering" type="boolean" help="number schema subscripts" truevalue="--numbering" falsevalue=""/> + <section name="sec_B" title="nonpolar ; hydrophobic"> + <param name="f_A" type="color" label="Color for A" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_A" type="color" label="Text color for A" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_F" type="color" label="Color for F" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_F" type="color" label="Text color for F" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_I" type="color" label="Color for I" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_I" type="color" label="Text color for I" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_L" type="color" label="Color for L" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_L" type="color" label="Text color for L" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_M" type="color" label="Color for M" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_M" type="color" label="Text color for M" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_P" type="color" label="Color for P" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_P" type="color" label="Text color for P" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_V" type="color" label="Color for V" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_V" type="color" label="Text color for V" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_W" type="color" label="Color for W" value="#ffcc33"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_W" type="color" label="Text color for W" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + </section> + <section name="sec_C" title="polar ; uncharged"> + <param name="f_C" type="color" label="Color for C" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_C" type="color" label="Text color for C" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_G" type="color" label="Color for G" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_G" type="color" label="Text color for G" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_N" type="color" label="Color for N" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_N" type="color" label="Text color for N" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_Q" type="color" label="Color for Q" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_Q" type="color" label="Text color for Q" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_S" type="color" label="Color for S" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_S" type="color" label="Text color for S" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_T" type="color" label="Color for T" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_T" type="color" label="Text color for T" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_Y" type="color" label="Color for Y" value="#b5b5b5"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_Y" type="color" label="Text color for Y" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + </section> + <section name="sec_D" title="polar ; acidic (negatively charged)"> + <param name="f_D" type="color" label="Color for D" value="#db270f"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_D" type="color" label="Text color for D" value="#FFFFFF"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_E" type="color" label="Color for E" value="#db270f"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_E" type="color" label="Text color for E" value="#FFFFFF"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + </section> + <section name="sec_E" title="polar ; basic (positive charge)"> + <param name="f_H" type="color" label="Color for H" value="#12d5fc"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_H" type="color" label="Text color for H" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_K" type="color" label="Color for K" value="#12d5fc"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_K" type="color" label="Text color for K" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="f_R" type="color" label="Color for R" value="#12d5fc"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + <param name="t_R" type="color" label="Text color for R" value="#000000"> + <sanitizer> + <valid initial="string.ascii_letters,string.digits"> + <add value="#"/> + </valid> + </sanitizer> + </param> + </section> + </inputs> + <outputs> + <data format="png" name="output" label="_helicalWheel.png"/> + </outputs> + <help><![CDATA[ +**What it does** +INPUT : Peptide Sequence +PARAMETERS : +primary parameters : +> Paste in exact sequence to be plotted - Input Sequence of desired helical wheel plot +> Label Start Number - Numerical value that represents the beginning of the sequence (default 1) +> Amino Acid Text Size - Size of text for helical wheel (default 32) +> Rotation - Degrees to rotate helical wheel (defaul 90) +color parameters : +> Background Color and Text Color Selections +METHOD : Using the core features from the modlAMP python module, a helical wheel projection is constructed. +OUTPUT : _helicalWheel.png +NOTES : Peptide lengths longer than 36 residues will not properly graph. +]]></help> + <citations> + <citation type="doi">10.1093/bioinformatics/btx285</citation> + <citation type="bibtex"> + @unpublished{galaxyTools, + author = {C. Ross}, + title = {CPT Galaxy Tools}, + year = {2020-}, + note = {https://github.com/tamu-cpt/galaxy-tools/} + } + </citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,74 @@ +<macros> + <xml name="requirements"> + <requirements> + <requirement type="package">progressivemauve</requirement> + <!--<requirement type="package" version="2.7">python</requirement>--> + <requirement type="package" version="0.6.4">bcbiogff</requirement> + <yield/> + </requirements> + </xml> + <token name="@WRAPPER_VERSION@">2.4.0</token> + <xml name="citation/progressive_mauve"> + <citation type="doi">10.1371/journal.pone.0011147</citation> + </xml> + <xml name="citation/gepard"> + <citation type="doi">10.1093/bioinformatics/btm039</citation> + </xml> + <token name="@XMFA_INPUT@"> + '$xmfa' + </token> + <xml name="xmfa_input" token_formats="xmfa"> + <param type="data" format="@FORMATS@" name="xmfa" label="XMFA MSA"/> + </xml> + <token name="@XMFA_FA_INPUT@"> + '$sequences' + </token> + <xml name="xmfa_fa_input"> + <param type="data" format="fasta" name="sequences" label="Sequences in alignment" help="These sequences should be the SAME DATASET that was used in the progressiveMauve run. Failing that, they should be provided in the same order as in original progressiveMauve run"/> + </xml> + <xml name="genome_selector"> + <conditional name="reference_genome"> + <param name="reference_genome_source" type="select" label="Reference Genome"> + <option value="history" selected="True">From History</option> + <option value="cached">Locally Cached</option> + </param> + <when value="cached"> + <param name="fasta_indexes" type="select" label="Source FASTA Sequence"> + <options from_data_table="all_fasta"/> + </param> + </when> + <when value="history"> + <param name="genome_fasta" type="data" format="fasta" label="Source FASTA Sequence"/> + </when> + </conditional> + </xml> + <xml name="gff3_input"> + <param label="GFF3 Annotations" name="gff3_data" type="data" format="gff3"/> + </xml> + <xml name="input/gff3+fasta"> + <expand macro="gff3_input"/> + <expand macro="genome_selector"/> + </xml> + <token name="@INPUT_GFF@"> + '$gff3_data' + </token> + <token name="@INPUT_FASTA@"> + #if str($reference_genome.reference_genome_source) == 'cached': + '${reference_genome.fasta_indexes.fields.path}' + #else if str($reference_genome.reference_genome_source) == 'history': + genomeref.fa + #end if + </token> + <token name="@GENOME_SELECTOR_PRE@"> + #if $reference_genome.reference_genome_source == 'history': + ln -s '$reference_genome.genome_fasta' genomeref.fa; + #end if + </token> + <token name="@GENOME_SELECTOR@"> + #if str($reference_genome.reference_genome_source) == 'cached': + '${reference_genome.fasta_indexes.fields.path}' + #else if str($reference_genome.reference_genome_source) == 'history': + genomeref.fa + #end if + </token> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotWheels/core.py Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,3228 @@ +# -*- coding: utf-8 -*- +""" +.. currentmodule:: modlamp.core + +.. moduleauthor:: modlab Alex Mueller ETH Zurich <alex.mueller@pharma.ethz.ch> + +Core helper functions and classes for other modules. The two main classes are: + +============================= ======================================================================================= +Class Characteristics +============================= ======================================================================================= +:py:class:`BaseSequence` Base class inheriting to all sequence classes in the module :py:mod:`modlamp.sequences` +:py:class:`BaseDescriptor` Base class inheriting to the two descriptor classes in :py:mod:`modlamp.descriptors` +============================= ======================================================================================= +""" + +import os +import random +import re + +import numpy as np +import pandas as pd +import collections +import operator +from scipy.spatial import distance +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from sklearn.utils import shuffle + +__author__ = "Alex Müller, Gisela Gabernet" +__docformat__ = "restructuredtext en" + + +class BaseSequence(object): + """Base class for sequence classes in the module :mod:`modlamp.sequences`. + It contains amino acid probabilities for different sequence generation classes. + + The following amino acid probabilities are used: (extracted from the + `APD3 <http://aps.unmc.edu/AP/statistic/statistic.php>`_, March 17, 2016) + + === ==== ====== ========= ========== + AA rand AMP AMPnoCM randnoCM + === ==== ====== ========= ========== + A 0.05 0.0766 0.0812275 0.05555555 + C 0.05 0.071 0.0 0.0 + D 0.05 0.026 0.0306275 0.05555555 + E 0.05 0.0264 0.0310275 0.05555555 + F 0.05 0.0405 0.0451275 0.05555555 + G 0.05 0.1172 0.1218275 0.05555555 + H 0.05 0.021 0.0256275 0.05555555 + I 0.05 0.061 0.0656275 0.05555555 + K 0.05 0.0958 0.1004275 0.05555555 + L 0.05 0.0838 0.0884275 0.05555555 + M 0.05 0.0123 0.0 0.0 + N 0.05 0.0386 0.0432275 0.05555555 + P 0.05 0.0463 0.0509275 0.05555555 + Q 0.05 0.0251 0.0297275 0.05555555 + R 0.05 0.0545 0.0591275 0.05555555 + S 0.05 0.0613 0.0659275 0.05555555 + T 0.05 0.0455 0.0501275 0.05555555 + V 0.05 0.0572 0.0618275 0.05555555 + W 0.05 0.0155 0.0201275 0.05555555 + Y 0.05 0.0244 0.0290275 0.05555555 + === ==== ====== ========= ========== + + """ + + def __init__(self, seqnum, lenmin=7, lenmax=28): + """ + :param seqnum: number of sequences to generate + :param lenmin: minimal length of the generated sequences + :param lenmax: maximal length of the generated sequences + :return: attributes :py:attr:`seqnum`, :py:attr:`lenmin` and :py:attr:`lenmax`. + :Example: + + >>> b = BaseSequence(10, 7, 28) + >>> b.seqnum + 10 + >>> b.lenmin + 7 + >>> b.lenmax + 28 + """ + self.sequences = list() + self.names = list() + self.lenmin = int(lenmin) + self.lenmax = int(lenmax) + self.seqnum = int(seqnum) + + # AA classes: + self.AA_hyd = ["G", "A", "L", "I", "V"] + self.AA_basic = ["K", "R"] + self.AA_acidic = ["D", "E"] + self.AA_aroma = ["W", "Y", "F"] + self.AA_polar = ["S", "T", "Q", "N"] + # AA labels: + self.AAs = [ + "A", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "M", + "N", + "P", + "Q", + "R", + "S", + "T", + "V", + "W", + "Y", + ] + # AA probability from the APD3 database: + self.prob_AMP = [ + 0.0766, + 0.071, + 0.026, + 0.0264, + 0.0405, + 0.1172, + 0.021, + 0.061, + 0.0958, + 0.0838, + 0.0123, + 0.0386, + 0.0463, + 0.0251, + 0.0545, + 0.0613, + 0.0455, + 0.0572, + 0.0155, + 0.0244, + ] + # AA probability from the APD2 database without Cys and Met (synthesis reasons) + self.prob_AMPnoCM = [ + 0.081228, + 0.0, + 0.030627, + 0.031027, + 0.045128, + 0.121828, + 0.025627, + 0.065628, + 0.100428, + 0.088428, + 0.0, + 0.043228, + 0.050928, + 0.029728, + 0.059128, + 0.065927, + 0.050128, + 0.061828, + 0.020128, + 0.029028, + ] + # equal AA probabilities: + self.prob = [ + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + 0.05, + ] + # equal AA probabilities but 0 for Cys and Met: + self.prob_randnoCM = [ + 0.05555555555, + 0.0, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.0, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + 0.05555555555, + ] + + # AA probability from the linear CancerPPD peptides: + self.prob_ACP = [ + 0.14526966, + 0.0, + 0.00690031, + 0.00780824, + 0.06991102, + 0.04957327, + 0.01725077, + 0.05647358, + 0.27637552, + 0.17759216, + 0.00998729, + 0.00798983, + 0.01307427, + 0.00381333, + 0.02941711, + 0.02651171, + 0.0154349, + 0.04013074, + 0.0406755, + 0.00581079, + ] + + # AA probabilities for perfect amphipathic helix of different arc sizes + self.prob_amphihel = [ + [ + 0.04545455, + 0.0, + 0.04545454, + 0.04545455, + 0.0, + 0.04545455, + 0.04545455, + 0.0, + 0.25, + 0.0, + 0.0, + 0.04545454, + 0.04545455, + 0.04545454, + 0.25, + 0.04545454, + 0.04545454, + 0.0, + 0.0, + 0.04545454, + ], + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.16666667, + 0.0, + 0.0, + 0.16666667, + 0.0, + 0.16666667, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.16666667, + 0.16666667, + (1.0 - 0.16666667 * 5), + ], + ] + + # helical ACP AA probabilities, depending on the position of the AA in the helix. + self.prob_ACPhel = np.array( + [ + [ + 0.0483871, + 0.0, + 0.0, + 0.0483871, + 0.01612903, + 0.12903226, + 0.03225807, + 0.09677419, + 0.19354839, + 0.5, + 0.0483871, + 0.11290323, + 0.1, + 0.18518519, + 0.07843137, + 0.12, + 0.17073172, + 0.16666667, + ], + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.02439024, + 0.19444444, + ], + [ + 0.0, + 0.01612903, + 0.0, + 0.27419355, + 0.01612903, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ], + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06451613, + 0.0, + 0.01612903, + 0.0483871, + 0.01612903, + 0.0, + 0.01851852, + 0.0, + 0.0, + 0.0, + 0.0, + ], + [ + 0.16129032, + 0.0483871, + 0.30645161, + 0.0, + 0.0483871, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.01612903, + 0.0, + 0.09677419, + 0.06666667, + 0.01851852, + 0.0, + 0.02, + 0.14634146, + 0.0, + ], + [ + 0.64516129, + 0.0, + 0.17741936, + 0.14516129, + 0.0, + 0.01612903, + 0.25806452, + 0.11290323, + 0.06451613, + 0.08064516, + 0.22580645, + 0.03225807, + 0.06666667, + 0.2037037, + 0.1372549, + 0.1, + 0.0, + 0.05555556, + ], + [ + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.03225807, + 0.0, + 0.0, + 0.20967742, + 0.0, + 0.0, + 0.0, + 0.16, + 0.0, + 0.0, + ], + [ + 0.0483871, + 0.11290323, + 0.01612903, + 0.08064516, + 0.33870968, + 0.27419355, + 0.0, + 0.0483871, + 0.14516129, + 0.06451613, + 0.03225807, + 0.06451613, + 0.18333333, + 0.0, + 0.0, + 0.1, + 0.26829268, + 0.0, + ], + [ + 0.0, + 0.03225807, + 0.01612903, + 0.12903226, + 0.12903226, + 0.0, + 0.38709677, + 0.33870968, + 0.0483871, + 0.03225807, + 0.41935484, + 0.08064516, + 0.0, + 0.03703704, + 0.29411765, + 0.04, + 0.02439024, + 0.02777778, + ], + [ + 0.0483871, + 0.70967742, + 0.12903226, + 0.0483871, + 0.09677419, + 0.32258064, + 0.20967742, + 0.06451613, + 0.11290323, + 0.06451613, + 0.03225807, + 0.03225807, + 0.28333333, + 0.24074074, + 0.03921569, + 0.28, + 0.07317073, + 0.22222222, + ], + [ + 0.0, + 0.01612903, + 0.01612903, + 0.0483871, + 0.01612903, + 0.03225807, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03333333, + 0.0, + 0.01960784, + 0.02, + 0.0, + 0.0, + ], + [ + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.03225807, + 0.0, + 0.0, + 0.0, + 0.01960784, + 0.02, + 0.0, + 0.0, + ], + [ + 0.0, + 0.0, + 0.14516129, + 0.01612903, + 0.03225807, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.12962963, + 0.17647059, + 0.0, + 0.0, + 0.0, + ], + [ + 0.0, + 0.0, + 0.01612903, + 0.01612903, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.01851852, + 0.0, + 0.0, + 0.0, + 0.0, + ], + [ + 0.0, + 0.01612903, + 0.01612903, + 0.0, + 0.01612903, + 0.0, + 0.01612903, + 0.0, + 0.01612903, + 0.01612903, + 0.01612903, + 0.01612903, + 0.0, + 0.01851852, + 0.01960784, + 0.0, + 0.04878049, + 0.0, + ], + [ + 0.01612903, + 0.0, + 0.01612903, + 0.12903226, + 0.03225807, + 0.03225807, + 0.0483871, + 0.17741936, + 0.0, + 0.03225807, + 0.09677419, + 0.0483871, + 0.01666667, + 0.0, + 0.15686274, + 0.1, + 0.0, + 0.05555556, + ], + [ + 0.01612903, + 0.01612903, + 0.0, + 0.01612903, + 0.0483871, + 0.01612903, + 0.0, + 0.01612903, + 0.0, + 0.01612903, + 0.01612903, + 0.11290323, + 0.0, + 0.01851852, + 0.03921569, + 0.02, + 0.0, + 0.05555556, + ], + [ + 0.01612903, + 0.01612903, + 0.01612903, + 0.01612903, + 0.20967742, + 0.16129032, + 0.01612903, + 0.0483871, + 0.33870968, + 0.16129032, + 0.0, + 0.14516129, + 0.25, + 0.11111111, + 0.01960784, + 0.02, + 0.21951219, + 0.22222222, + ], + [ + 0.0, + 0.0, + 0.12903226, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.02439024, + 0.0, + ], + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01612903, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ], + ] + ) + + def save_fasta(self, filename, names=False): + """Method to save generated sequences in a ``.FASTA`` formatted file. + + :param filename: output filename in which the sequences from :py:attr:`sequences` are safed in fasta format. + :param names: {bool} whether sequence names from :py:attr:`names` should be saved as sequence identifiers + :return: a FASTA formatted file containing the generated sequences + :Example: + + >>> b = BaseSequence(2) + >>> b.sequences = ['KLLSLSLALDLLS', 'KLPERTVVNSSDF'] + >>> b.names = ['Sequence1', 'Sequence2'] + >>> b.save_fasta('/location/of/fasta/file.fasta', names=True) + """ + if names: + save_fasta(filename, self.sequences, self.names) + else: + save_fasta(filename, self.sequences) + + def mutate_AA(self, nr, prob): + """Method to mutate with **prob** probability a **nr** of positions per sequence randomly. + + :param nr: number of mutations to perform per sequence + :param prob: probability of mutating a sequence + :return: mutated sequences in the attribute :py:attr:`sequences`. + :Example: + + >>> b = BaseSequence(1) + >>> b.sequences = ['IAKAGRAIIK'] + >>> b.mutate_AA(3, 1.) + >>> b.sequences + ['NAKAGRAWIK'] + """ + for s in range(len(self.sequences)): + # mutate: yes or no? prob = mutation probability + mutate = np.random.choice([1, 0], 1, p=[prob, 1 - float(prob)]) + if mutate == 1: + seq = list(self.sequences[s]) + cnt = 0 + while cnt < nr: # mutate "nr" AA + seq[random.choice(range(len(seq)))] = random.choice(self.AAs) + cnt += 1 + self.sequences[s] = "".join(seq) + + def filter_duplicates(self): + """Method to filter duplicates in the sequences from the class attribute :py:attr:`sequences` + + :return: filtered sequences list in the attribute :py:attr:`sequences` and corresponding names. + :Example: + + >>> b = BaseSequence(4) + >>> b.sequences = ['KLLKLLKKLLKLLK', 'KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK', 'KLAKLAKKLAKLAK'] + >>> b.filter_duplicates() + >>> b.sequences + ['KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK'] + + .. versionadded:: v2.2.5 + """ + if not self.names: + self.names = ["Seq_" + str(i) for i in range(len(self.sequences))] + df = pd.DataFrame( + list(zip(self.sequences, self.names)), columns=["Sequences", "Names"] + ) + df = df.drop_duplicates( + "Sequences", "first" + ) # keep first occurrence of duplicate + self.sequences = df["Sequences"].get_values().tolist() + self.names = df["Names"].get_values().tolist() + + def keep_natural_aa(self): + """Method to filter out sequences that do not contain natural amino acids. If the sequence contains a character + that is not in ``['A','C','D,'E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']``. + + :return: filtered sequence list in the attribute :py:attr:`sequences`. The other attributes are also filtered + accordingly (if present). + :Example: + + >>> b = BaseSequence(2) + >>> b.sequences = ['BBBsdflUasUJfBJ', 'GLFDIVKKVVGALGSL'] + >>> b.keep_natural_aa() + >>> b.sequences + ['GLFDIVKKVVGALGSL'] + """ + natural_aa = [ + "A", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "M", + "N", + "P", + "Q", + "R", + "S", + "T", + "V", + "W", + "Y", + ] + + seqs = [] + names = [] + + for i, s in enumerate(self.sequences): + seq = list(s.upper()) + if all(c in natural_aa for c in seq): + seqs.append(s.upper()) + if hasattr(self, "names") and self.names: + names.append(self.names[i]) + + self.sequences = seqs + self.names = names + + def filter_aa(self, amino_acids): + """Method to filter out corresponding names and descriptor values of sequences with given amino acids in the + argument list *aminoacids*. + + :param amino_acids: {list} amino acids to be filtered + :return: filtered list of sequences names in the corresponding attributes. + :Example: + + >>> b = BaseSequence(3) + >>> b.sequences = ['AAALLLIIIKKK', 'CCEERRT', 'LLVVIIFFFQQ'] + >>> b.filter_aa(['C']) + >>> b.sequences + ['AAALLLIIIKKK', 'LLVVIIFFFQQ'] + """ + + pattern = re.compile("|".join(amino_acids)) + seqs = [] + names = [] + + for i, s in enumerate(self.sequences): + if not pattern.search(s): + seqs.append(s) + if hasattr(self, "names") and self.names: + names.append(self.names[i]) + + self.sequences = seqs + self.names = names + + def clean(self): + """Method to clean / clear / empty the attributes :py:attr:`sequences` and :py:attr:`names`. + + :return: freshly initialized, empty class attributes. + """ + self.__init__(self.seqnum, self.lenmin, self.lenmax) + + +class BaseDescriptor(object): + """ + Base class inheriting to both peptide descriptor classes :py:class:`modlamp.descriptors.GlobalDescriptor` and + :py:class:`modlamp.descriptors.PeptideDescriptor`. + """ + + def __init__(self, seqs): + """ + :param seqs: a ``.FASTA`` file with sequences, a list / array of sequences or a single sequence as string to + calculate the descriptor values for. + :return: initialized attributes :py:attr:`sequences` and :py:attr:`names`. + :Example: + + >>> AMP = BaseDescriptor('KLLKLLKKLLKLLK','pepCATS') + >>> AMP.sequences + ['KLLKLLKKLLKLLK'] + >>> seqs = BaseDescriptor('/Path/to/file.fasta', 'eisenberg') # load sequences from .fasta file + >>> seqs.sequences + ['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'...] + """ + if type(seqs) == list and seqs[0].isupper(): + self.sequences = [s.strip() for s in seqs] + self.names = [] + elif type(seqs) == np.ndarray and seqs[0].isupper(): + self.sequences = [s.strip() for s in seqs.tolist()] + self.names = [] + elif type(seqs) == str and seqs.isupper(): + self.sequences = [seqs.strip()] + self.names = [] + elif os.path.isfile(seqs): + if seqs.endswith(".fasta"): # read .fasta file + self.sequences, self.names = read_fasta(seqs) + elif seqs.endswith(".csv"): # read .csv file with sequences every line + with open(seqs) as f: + self.sequences = list() + cntr = 0 + self.names = [] + for line in f: + if line.isupper(): + self.sequences.append(line.strip()) + self.names.append("seq_" + str(cntr)) + cntr += 1 + else: + print("Sorry, currently only .fasta or .csv files can be read!") + else: + print( + "%s does not exist, is not a valid list of AA sequences or is not a valid sequence string" + % seqs + ) + + self.descriptor = np.array([[]]) + self.target = np.array([], dtype="int") + self.scaler = None + self.featurenames = [] + + def read_fasta(self, filename): + """Method for loading sequences from a ``.FASTA`` formatted file into the attributes :py:attr:`sequences` and + :py:attr:`names`. + + :param filename: {str} ``.FASTA`` file with sequences and headers to read + :return: {list} sequences in the attribute :py:attr:`sequences` with corresponding sequence names in + :py:attr:`names`. + """ + self.sequences, self.names = read_fasta(filename) + + def save_fasta(self, filename, names=False): + """Method for saving sequences from :py:attr:`sequences` to a ``.FASTA`` formatted file. + + :param filename: {str} filename of the output ``.FASTA`` file + :param names: {bool} whether sequence names from self.names should be saved as sequence identifiers + :return: a FASTA formatted file containing the generated sequences + """ + if names: + save_fasta(filename, self.sequences, self.names) + else: + save_fasta(filename, self.sequences) + + def count_aa(self, scale="relative", average=False, append=False): + """Method for producing the amino acid distribution for the given sequences as a descriptor + + :param scale: {'absolute' or 'relative'} defines whether counts or frequencies are given for each AA + :param average: {boolean} whether the averaged amino acid counts for all sequences should be returned + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: the amino acid distributions for every sequence individually in the attribute :py:attr:`descriptor` + :Example: + + >>> AMP = PeptideDescriptor('ACDEFGHIKLMNPQRSTVWY') # aa_count() does not depend on the descriptor scale + >>> AMP.count_aa() + >>> AMP.descriptor + array([[ 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, ... ]]) + >>> AMP.descriptor.shape + (1, 20) + + .. seealso:: :py:func:`modlamp.core.count_aa()` + """ + desc = list() + for seq in self.sequences: + od = count_aas(seq, scale) + desc.append(list(od.values())) + + desc = np.array(desc) + self.featurenames = list(od.keys()) + + if append: + self.descriptor = np.hstack((self.descriptor, desc)) + elif average: + self.descriptor = np.mean(desc, axis=0) + else: + self.descriptor = desc + + def count_ngrams(self, n): + """Method for producing n-grams of all sequences in self.sequences + + :param n: {int or list of ints} defines whether counts or frequencies are given for each AA + :return: {dict} dictionary with n-grams as keys and their counts in the sequence as values in :py:attr:`descriptor` + :Example: + + >>> D = PeptideDescriptor('GLLDFLSLAALSLDKLVKKGALS') + >>> D.count_ngrams([2, 3]) + >>> D.descriptor + {'LS': 3, 'LD': 2, 'LSL': 2, 'AL': 2, ..., 'LVK': 1} + + .. seealso:: :py:func:`modlamp.core.count_ngrams()` + """ + ngrams = dict() + for seq in self.sequences: + d = count_ngrams(seq, n) + for k, v in d.items(): + if k in ngrams.keys(): + ngrams[k] += v + else: + ngrams[k] = v + self.descriptor = ngrams + + def feature_scaling(self, stype="standard", fit=True): + """Method for feature scaling of the calculated descriptor matrix. + + :param stype: {'standard' or 'minmax'} type of scaling to be used + :param fit: {boolean} defines whether the used scaler is first fitting on the data (True) or + whether the already fitted scaler in :py:attr:`scaler` should be used to transform (False). + :return: scaled descriptor values in :py:attr:`descriptor` + :Example: + + >>> D.descriptor + array([[0.155],[0.34],[0.16235294],[-0.08842105],[0.116]]) + >>> D.feature_scaling(type='minmax',fit=True) + array([[0.56818182],[1.],[0.5853447],[0.],[0.47714988]]) + """ + if stype in ["standard", "minmax"]: + if stype == "standard": + self.scaler = StandardScaler() + elif stype == "minmax": + self.scaler = MinMaxScaler() + + if fit: + self.descriptor = self.scaler.fit_transform(self.descriptor) + else: + self.descriptor = self.scaler.transform(self.descriptor) + else: + print("Unknown scaler type!\nAvailable: 'standard', 'minmax'") + + def feature_shuffle(self): + """Method for shuffling feature columns randomly. + + :return: descriptor matrix with shuffled feature columns in :py:attr:`descriptor` + :Example: + + >>> D.descriptor + array([[0.80685625,167.05234375,39.56818125,-0.26338667,155.16888667,33.48778]]) + >>> D.feature_shuffle() + array([[155.16888667,-0.26338667,167.05234375,0.80685625,39.56818125,33.48778]]) + """ + self.descriptor = shuffle(self.descriptor.transpose()).transpose() + + def sequence_order_shuffle(self): + """Method for shuffling sequence order in the attribute :py:attr:`sequences`. + + :return: sequences in :py:attr:`sequences` with shuffled order in the list. + :Example: + + >>> D.sequences + ['LILRALKGAARALKVA','VKIAKIALKIIKGLG','VGVRLIKGIGRVARGAI','LRGLRGVIRGGKAIVRVGK','GGKLVRLIARIGKGV'] + >>> D.sequence_order_shuffle() + >>> D.sequences + ['VGVRLIKGIGRVARGAI','LILRALKGAARALKVA','LRGLRGVIRGGKAIVRVGK','GGKLVRLIARIGKGV','VKIAKIALKIIKGLG'] + """ + self.sequences = shuffle(self.sequences) + + def random_selection(self, num): + """Method to randomly select a specified number of sequences (with names and descriptors if present) out of a given + descriptor instance. + + :param num: {int} number of entries to be randomly selected + :return: updated instance + :Example: + + >>> h = Helices(7, 28, 100) + >>> h.generate_helices() + >>> desc = PeptideDescriptor(h.sequences, 'eisenberg') + >>> desc.calculate_moment() + >>> len(desc.sequences) + 100 + >>> len(desc.descriptor) + 100 + >>> desc.random_selection(10) + >>> len(desc.descriptor) + 10 + >>> len(desc.descriptor) + 10 + + .. versionadded:: v2.2.3 + """ + + sel = np.random.choice(len(self.sequences), size=num, replace=False) + self.sequences = np.array(self.sequences)[sel].tolist() + if hasattr(self, "descriptor") and self.descriptor.size: + self.descriptor = self.descriptor[sel] + if hasattr(self, "names") and self.names: + self.names = np.array(self.names)[sel].tolist() + if hasattr(self, "target") and self.target.size: + self.target = self.target[sel] + + def minmax_selection(self, iterations, distmetric="euclidean", seed=0): + """Method to select a specified number of sequences according to the minmax algorithm. + + :param iterations: {int} Number of sequences to retrieve. + :param distmetric: Distance metric to calculate the distances between the sequences in descriptor space. + Choose from 'euclidean' or 'minkowsky'. + :param seed: {int} Set a random seed for numpy to pick the first sequence. + :return: updated instance + + .. seealso:: **SciPy** http://docs.scipy.org/doc/scipy/reference/spatial.distance.html + """ + + # Storing M into pool, where selections get deleted + pool = self.descriptor # Store pool where selections get deleted + minmaxidx = list() # Store original indices of selections to return + + # Randomly selecting first peptide into the sele + np.random.seed(seed) + idx = int(np.random.random_integers(0, len(pool), 1)) + sele = pool[idx : idx + 1, :] + minmaxidx.append( + int(*np.where(np.all(self.descriptor == pool[idx : idx + 1, :], axis=1))) + ) + + # Deleting peptide in selection from pool + pool = np.delete(pool, idx, axis=0) + + for i in range(iterations - 1): + # Calculating distance from sele to the rest of the peptides + dist = distance.cdist(pool, sele, distmetric) + + # Choosing maximal distances for every sele instance + maxidx = np.argmax(dist, axis=0) + maxcols = np.max(dist, axis=0) + + # Choosing minimal distance among the maximal distances + minmax = np.argmin(maxcols) + maxidx = int(maxidx[minmax]) + + # Adding it to selection and removing from pool + sele = np.append(sele, pool[maxidx : maxidx + 1, :], axis=0) + pool = np.delete(pool, maxidx, axis=0) + minmaxidx.append( + int( + *np.where( + np.all(self.descriptor == pool[maxidx : maxidx + 1, :], axis=1) + ) + ) + ) + + self.sequences = np.array(self.sequences)[minmaxidx].tolist() + if hasattr(self, "descriptor") and self.descriptor.size: + self.descriptor = self.descriptor[minmaxidx] + if hasattr(self, "names") and self.names: + self.names = np.array(self.names)[minmaxidx].tolist() + if hasattr(self, "target") and self.target.size: + self.target = self.descriptor[minmaxidx] + + def filter_sequences(self, sequences): + """Method to filter out entries for given sequences in *sequences* out of a descriptor instance. All + corresponding attribute values of these sequences (e.g. in :py:attr:`descriptor`, :py:attr:`name`) are deleted + as well. The method returns an updated descriptor instance. + + :param sequences: {list} sequences to be filtered out of the whole instance, including corresponding data + :return: updated instance without filtered sequences + :Example: + + >>> sequences = ['KLLKLLKKLLKLLK', 'ACDEFGHIK', 'GLFDIVKKVV', 'GLFDIVKKVVGALG', 'GLFDIVKKVVGALGSL'] + >>> desc = PeptideDescriptor(sequences, 'pepcats') + >>> desc.calculate_crosscorr(7) + >>> len(desc.descriptor) + 5 + >>> desc.filter_sequences('KLLKLLKKLLKLLK') + >>> len(desc.descriptor) + 4 + >>> desc.sequences + ['ACDEFGHIK', 'GLFDIVKKVV', 'GLFDIVKKVVGALG', 'GLFDIVKKVVGALGSL'] + """ + indices = list() + if isinstance( + sequences, str + ): # check if sequences is only one sequence string and convert it to a list + sequences = [sequences] + for s in sequences: # get indices of queried sequences + indices.append(self.sequences.index(s)) + + self.sequences = np.delete(np.array(self.sequences), indices, 0).tolist() + if hasattr(self, "descriptor") and self.descriptor.size: + self.descriptor = np.delete(self.descriptor, indices, 0) + if hasattr(self, "names") and self.names: + self.names = np.delete(np.array(self.names), indices, 0).tolist() + if hasattr(self, "target") and self.target.size: + self.target = np.delete(self.target, indices, 0) + + def filter_values(self, values, operator="=="): + """Method to filter the descriptor matrix in the attribute :py:attr:`descriptor` for a given list of values (same + size as the number of features in the descriptor matrix!) The operator option tells the method whether to + filter for values equal, lower, higher ect. to the given values in the *values* array. + + :param values: {list} values to filter the attribute :py:attr:`descriptor` for + :param operator: {str} filter criterion, available the operators ``==``, ``<``, ``>``, ``<=``and ``>=``. + :return: descriptor matrix and updated sequences containing only entries with descriptor values given in + *values* in the corresponding attributes. + :Example: + + >>> desc.descriptor # desc = BaseDescriptor instance + array([[ 0.7666517 ], + [ 0.38373498]]) + >>> desc.filter_values([0.5], '<') + >>> desc.descriptor + array([[ 0.38373498]]) + """ + dim = self.descriptor.shape[1] + for d in range(dim): # for all the features in self.descriptor + if operator == "==": + indices = np.where(self.descriptor[:, d] == values[d])[0] + elif operator == "<": + indices = np.where(self.descriptor[:, d] < values[d])[0] + elif operator == ">": + indices = np.where(self.descriptor[:, d] > values[d])[0] + elif operator == "<=": + indices = np.where(self.descriptor[:, d] <= values[d])[0] + elif operator == ">=": + indices = np.where(self.descriptor[:, d] >= values[d])[0] + else: + raise KeyError( + "available operators: ``==``, ``<``, ``>``, ``<=``and ``>=``" + ) + + # filter descriptor matrix, sequence list and names list according to obtained indices + self.sequences = np.array(self.sequences)[indices].tolist() + if hasattr(self, "descriptor") and self.descriptor.size: + self.descriptor = self.descriptor[indices] + if hasattr(self, "names") and self.names: + self.names = np.array(self.names)[indices].tolist() + if hasattr(self, "target") and self.target.size: + self.target = self.target[indices] + + def filter_aa(self, amino_acids): + """Method to filter out corresponding names and descriptor values of sequences with given amino acids in the + argument list *aminoacids*. + + :param amino_acids: list of amino acids to be filtered + :return: filtered list of sequences, descriptor values, target values and names in the corresponding attributes. + :Example: + + >>> b = BaseSequence(3) + >>> b.sequences = ['AAALLLIIIKKK', 'CCEERRT', 'LLVVIIFFFQQ'] + >>> b.filter_aa(['C']) + >>> b.sequences + ['AAALLLIIIKKK', 'LLVVIIFFFQQ'] + """ + + pattern = re.compile("|".join(amino_acids)) + seqs = [] + desc = [] + names = [] + target = [] + + for i, s in enumerate(self.sequences): + if not pattern.search(s): + seqs.append(s) + if hasattr(self, "descriptor") and self.descriptor.size: + desc.append(self.descriptor[i]) + if hasattr(self, "names") and self.names: + names.append(self.names[i]) + if hasattr(self, "target") and self.target.size: + target.append(self.target[i]) + + self.sequences = seqs + self.names = names + self.descriptor = np.array(desc) + self.target = np.array(target, dtype="int") + + def filter_duplicates(self): + """Method to filter duplicates in the sequences from the class attribute :py:attr:`sequences` + + :return: filtered sequences list in the attribute :py:attr:`sequences` and corresponding names. + :Example: + + >>> b = BaseDescriptor(['KLLKLLKKLLKLLK', 'KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK', 'KLAKLAKKLAKLAK']) + >>> b.filter_duplicates() + >>> b.sequences + ['KLLKLLKKLLKLLK', 'KLAKLAKKLAKLAK'] + + .. versionadded:: v2.2.5 + """ + if not self.names: + self.names = ["Seq_" + str(i) for i in range(len(self.sequences))] + if not self.target: + self.target = [0] * len(self.sequences) + if not self.descriptor: + self.descriptor = np.zeros(len(self.sequences)) + df = pd.DataFrame( + np.array([self.sequences, self.names, self.descriptor, self.target]).T, + columns=["Sequences", "Names", "Descriptor", "Target"], + ) + df = df.drop_duplicates( + "Sequences", "first" + ) # keep first occurrence of duplicate + self.sequences = df["Sequences"].get_values().tolist() + self.names = df["Names"].get_values().tolist() + self.descriptor = df["Descriptor"].get_values() + self.target = df["Target"].get_values() + + def keep_natural_aa(self): + """Method to filter out sequences that do not contain natural amino acids. If the sequence contains a character + that is not in ['A','C','D,'E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']. + + :return: filtered sequence list in the attribute :py:attr:`sequences`. The other attributes are also filtered + accordingly (if present). + :Example: + + >>> b = BaseSequence(2) + >>> b.sequences = ['BBBsdflUasUJfBJ', 'GLFDIVKKVVGALGSL'] + >>> b.keep_natural_aa() + >>> b.sequences + ['GLFDIVKKVVGALGSL'] + """ + + natural_aa = [ + "A", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "M", + "N", + "P", + "Q", + "R", + "S", + "T", + "V", + "W", + "Y", + ] + + seqs = [] + desc = [] + names = [] + target = [] + + for i, s in enumerate(self.sequences): + seq = list(s.upper()) + if all(c in natural_aa for c in seq): + seqs.append(s.upper()) + if hasattr(self, "descriptor") and self.descriptor.size: + desc.append(self.descriptor[i]) + if hasattr(self, "names") and self.names: + names.append(self.names[i]) + if hasattr(self, "target") and self.target.size: + target.append(self.target[i]) + + self.sequences = seqs + self.names = names + self.descriptor = np.array(desc) + self.target = np.array(target, dtype="int") + + def load_descriptordata( + self, filename, delimiter=",", targets=False, skip_header=0 + ): + """Method to load any data file with sequences and descriptor values and save it to a new insatnce of the + class :class:`modlamp.descriptors.PeptideDescriptor`. + + .. note:: Headers are not considered. To skip initial lines in the file, use the *skip_header* option. + + :param filename: {str} filename of the data file to be loaded + :param delimiter: {str} column delimiter + :param targets: {boolean} whether last column in the file contains a target class vector + :param skip_header: {int} number of initial lines to skip in the file + :return: loaded sequences, descriptor values and targets in the corresponding attributes. + """ + data = np.genfromtxt(filename, delimiter=delimiter, skip_header=skip_header) + data = data[:, 1:] # skip sequences as they are "nan" when read as float + seqs = np.genfromtxt(filename, delimiter=delimiter, dtype="str") + seqs = seqs[:, 0] + if targets: + self.target = np.array(data[:, -1], dtype="int") + self.sequences = seqs + self.descriptor = data + + def save_descriptor(self, filename, delimiter=",", targets=None, header=None): + """Method to save the descriptor values to a .csv/.txt file + + :param filename: filename of the output file + :param delimiter: column delimiter + :param targets: target class vector to be added to descriptor (same length as :py:attr:`sequences`) + :param header: {str} header to be written at the beginning of the file (if ``None``: feature names are taken) + :return: output file with peptide names and descriptor values + """ + seqs = np.array(self.sequences, dtype="|S80")[:, np.newaxis] + ids = np.array(self.names, dtype="|S80")[:, np.newaxis] + if ids.shape == seqs.shape: + names = np.hstack((ids, seqs)) + else: + names = seqs + if targets and len(targets) == len(self.sequences): + target = np.array(targets)[:, np.newaxis] + data = np.hstack((names, self.descriptor, target)) + else: + data = np.hstack((names, self.descriptor)) + if not header: + featurenames = [["Sequence"]] + self.featurenames + header = ", ".join([f[0] for f in featurenames]) + np.savetxt(filename, data, delimiter=delimiter, fmt="%s", header=header) + + +def load_scale(scalename): + """Method to load scale values for a given amino acid scale + + :param scalename: amino acid scale name, for available scales see the + :class:`modlamp.descriptors.PeptideDescriptor()` documentation. + :return: amino acid scale values in dictionary format. + """ + # predefined amino acid scales dictionary + scales = { + "aasi": { + "A": [1.89], + "C": [1.73], + "D": [3.13], + "E": [3.14], + "F": [1.53], + "G": [2.67], + "H": [3], + "I": [1.97], + "K": [2.28], + "L": [1.74], + "M": [2.5], + "N": [2.33], + "P": [0.22], + "Q": [3.05], + "R": [1.91], + "S": [2.14], + "T": [2.18], + "V": [2.37], + "W": [2], + "Y": [2.01], + }, + "abhprk": { + "A": [0, 0, 0, 0, 0, 0], + "C": [0, 0, 0, 0, 0, 0], + "D": [1, 0, 0, 1, 0, 0], + "E": [1, 0, 0, 1, 0, 0], + "F": [0, 0, 1, 0, 1, 0], + "G": [0, 0, 0, 0, 0, 0], + "H": [0, 0, 0, 1, 1, 0], + "I": [0, 0, 1, 0, 0, 0], + "K": [0, 1, 0, 1, 0, 0], + "L": [0, 0, 1, 0, 0, 0], + "M": [0, 0, 1, 0, 0, 0], + "N": [0, 0, 0, 1, 0, 0], + "P": [0, 0, 0, 0, 0, 1], + "Q": [0, 0, 0, 1, 0, 0], + "R": [0, 1, 0, 1, 0, 0], + "S": [0, 0, 0, 1, 0, 0], + "T": [0, 0, 0, 1, 0, 0], + "V": [0, 0, 1, 0, 0, 0], + "W": [0, 0, 1, 0, 1, 0], + "Y": [0, 0, 0, 1, 1, 0], + }, + "argos": { + "I": [0.77], + "F": [1.2], + "V": [0.14], + "L": [2.3], + "W": [0.07], + "M": [2.3], + "A": [0.64], + "G": [-0.48], + "C": [0.25], + "Y": [-0.41], + "P": [-0.31], + "T": [-0.13], + "S": [-0.25], + "H": [-0.87], + "E": [-0.94], + "N": [-0.89], + "Q": [-0.61], + "D": [-1], + "K": [-1], + "R": [-0.68], + }, + "bulkiness": { + "A": [0.443], + "C": [0.551], + "D": [0.453], + "E": [0.557], + "F": [0.898], + "G": [0], + "H": [0.563], + "I": [0.985], + "K": [0.674], + "L": [0.985], + "M": [0.703], + "N": [0.516], + "P": [0.768], + "Q": [0.605], + "R": [0.596], + "S": [0.332], + "T": [0.677], + "V": [0.995], + "W": [1], + "Y": [0.801], + }, + "charge_phys": { + "A": [0.0], + "C": [-0.1], + "D": [-1.0], + "E": [-1.0], + "F": [0.0], + "G": [0.0], + "H": [0.1], + "I": [0.0], + "K": [1.0], + "L": [0.0], + "M": [0.0], + "N": [0.0], + "P": [0.0], + "Q": [0.0], + "R": [1.0], + "S": [0.0], + "T": [0.0], + "V": [0.0], + "W": [0.0], + "Y": [0.0], + }, + "charge_acid": { + "A": [0.0], + "C": [-0.1], + "D": [-1.0], + "E": [-1.0], + "F": [0.0], + "G": [0.0], + "H": [1.0], + "I": [0.0], + "K": [1.0], + "L": [0.0], + "M": [0.0], + "N": [0.0], + "P": [0.0], + "Q": [0.0], + "R": [1.0], + "S": [0.0], + "T": [0.0], + "V": [0.0], + "W": [0.0], + "Y": [0.0], + }, + "cougar": { + "A": [0.25, 0.62, 1.89], + "C": [0.208, 0.29, 1.73], + "D": [0.875, -0.9, 3.13], + "E": [0.833, -0.74, 3.14], + "F": [0.042, 1.2, 1.53], + "G": [1, 0.48, 2.67], + "H": [0.083, -0.4, 3], + "I": [0.667, 1.4, 1.97], + "K": [0.708, -1.5, 2.28], + "L": [0.292, 1.1, 1.74], + "M": [0, 0.64, 2.5], + "N": [0.667, -0.78, 2.33], + "P": [0.875, 0.12, 0.22], + "Q": [0.792, -0.85, 3.05], + "R": [0.958, -2.5, 1.91], + "S": [0.875, -0.18, 2.14], + "T": [0.583, -0.05, 2.18], + "V": [0.375, 1.1, 2.37], + "W": [0.042, 0.81, 2], + "Y": [0.5, 0.26, 2.01], + }, + "eisenberg": { + "I": [1.4], + "F": [1.2], + "V": [1.1], + "L": [1.1], + "W": [0.81], + "M": [0.64], + "A": [0.62], + "G": [0.48], + "C": [0.29], + "Y": [0.26], + "P": [0.12], + "T": [-0.05], + "S": [-0.18], + "H": [-0.4], + "E": [-0.74], + "N": [-0.78], + "Q": [-0.85], + "D": [-0.9], + "K": [-1.5], + "R": [-2.5], + }, + "ez": { + "A": [-0.29, 10.22, 4.67], + "C": [0.95, 13.69, 5.77], + "D": [1.19, 14.25, 8.98], + "E": [1.3, 14.66, 4.16], + "F": [-0.8, 19.67, 7.12], + "G": [-0.01, 13.86, 6], + "H": [0.75, 12.26, 2.77], + "I": [-0.56, 14.34, 10.69], + "K": [1.66, 11.11, 2.09], + "L": [-0.64, 17.34, 8.61], + "M": [-0.28, 18.04, 7.13], + "N": [0.89, 12.78, 6.28], + "P": [0.83, 18.09, 3.53], + "Q": [1.21, 10.46, 2.59], + "R": [1.55, 9.34, 4.68], + "S": [0.1, 13.86, 6], + "T": [0.01, 13.86, 6], + "V": [-0.47, 11.35, 4.97], + "W": [-0.85, 11.65, 7.2], + "Y": [-0.42, 13.04, 6.2], + }, + "flexibility": { + "A": [0.25], + "C": [0.208], + "D": [0.875], + "E": [0.833], + "F": [0.042], + "G": [1], + "H": [0.083], + "I": [0.667], + "K": [0.708], + "L": [0.292], + "M": [0.0], + "N": [0.667], + "P": [0.875], + "Q": [0.792], + "R": [0.958], + "S": [0.875], + "T": [0.583], + "V": [0.375], + "W": [0.042], + "Y": [0.5], + }, + "grantham": { + "A": [0, 8.1, 31], + "C": [2.75, 5.5, 55], + "D": [1.38, 13.0, 54], + "E": [0.92, 12.3, 83], + "F": [0, 5.2, 132], + "G": [0.74, 9.0, 3], + "H": [0.58, 10.4, 96], + "I": [0, 5.2, 111], + "K": [0.33, 11.3, 119], + "L": [0, 4.9, 111], + "M": [0, 5.7, 105], + "N": [1.33, 11.6, 56], + "P": [0.39, 8.0, 32.5], + "Q": [0.89, 10.5, 85], + "R": [0.65, 10.5, 124], + "S": [1.42, 9.2, 32], + "T": [0.71, 8.6, 61], + "V": [0, 5.9, 84], + "W": [0.13, 5.4, 170], + "Y": [0.20, 6.2, 136], + }, + "gravy": { + "I": [4.5], + "V": [4.2], + "L": [3.8], + "F": [2.8], + "C": [2.5], + "M": [1.9], + "A": [1.8], + "G": [-0.4], + "T": [-0.7], + "W": [-0.9], + "S": [-0.8], + "Y": [-1.3], + "P": [-1.6], + "H": [-3.2], + "E": [-3.5], + "Q": [-3.5], + "D": [-3.5], + "N": [-3.5], + "K": [-3.9], + "R": [-4.5], + }, + "hopp-woods": { + "A": [-0.5], + "C": [-1], + "D": [3], + "E": [3], + "F": [-2.5], + "G": [0], + "H": [-0.5], + "I": [-1.8], + "K": [3], + "L": [-1.8], + "M": [-1.3], + "N": [0.2], + "P": [0], + "Q": [0.2], + "R": [3], + "S": [0.3], + "T": [-0.4], + "V": [-1.5], + "W": [-3.4], + "Y": [-2.3], + }, + "isaeci": { + "A": [62.9, 0.05], + "C": [78.51, 0.15], + "D": [18.46, 1.25], + "E": [30.19, 1.31], + "F": [189.42, 0.14], + "G": [19.93, 0.02], + "H": [87.38, 0.56], + "I": [149.77, 0.09], + "K": [102.78, 0.53], + "L": [154.35, 0.1], + "M": [132.22, 0.34], + "N": [19.53, 1.36], + "P": [122.35, 0.16], + "Q": [17.87, 1.31], + "R": [52.98, 1.69], + "S": [19.75, 0.56], + "T": [59.44, 0.65], + "V": [120.91, 0.07], + "W": [179.16, 1.08], + "Y": [132.16, 0.72], + }, + "janin": { + "I": [1.2], + "F": [0.87], + "V": [1], + "L": [0.87], + "W": [0.59], + "M": [0.73], + "A": [0.59], + "G": [0.59], + "C": [1.4], + "Y": [-0.4], + "P": [-0.26], + "T": [-0.12], + "S": [0.02], + "H": [0.02], + "E": [-0.83], + "N": [-0.55], + "Q": [-0.83], + "D": [-0.69], + "K": [-2.4], + "R": [-1.8], + }, + "kytedoolittle": { + "I": [1.7], + "F": [1.1], + "V": [1.6], + "L": [1.4], + "W": [-0.14], + "M": [0.8], + "A": [0.77], + "G": [0.03], + "C": [1], + "Y": [-0.27], + "P": [-0.37], + "T": [-0.07], + "S": [-0.1], + "H": [-0.91], + "E": [-1], + "N": [-1], + "Q": [-1], + "D": [-1], + "K": [-1.1], + "R": [-1.3], + }, + "levitt_alpha": { + "A": [1.29], + "C": [1.11], + "D": [1.04], + "E": [1.44], + "F": [1.07], + "G": [0.56], + "H": [1.22], + "I": [0.97], + "K": [1.23], + "L": [1.3], + "M": [1.47], + "N": [0.9], + "P": [0.52], + "Q": [1.27], + "R": [0.96], + "S": [0.82], + "T": [0.82], + "V": [0.91], + "W": [0.99], + "Y": [0.72], + }, + "mss": { + "A": [13.02], + "C": [23.7067], + "D": [22.02], + "E": [20.0233], + "F": [23.5288], + "G": [1.01], + "H": [23.5283], + "I": [22.3611], + "K": [18.9756], + "L": [19.6944], + "M": [21.92], + "N": [21.8567], + "P": [19.0242], + "Q": [19.9689], + "R": [19.0434], + "S": [18.3533], + "T": [22.3567], + "V": [21.0267], + "W": [26.1975], + "Y": [24.1954], + }, + "msw": { + "A": [-0.73, 0.2, -0.62], + "C": [-0.66, 0.26, -0.27], + "D": [0.11, -1, -0.96], + "E": [0.24, -0.39, -0.04], + "F": [0.76, 0.85, -0.34], + "G": [-0.31, -0.28, -0.75], + "H": [0.84, 0.67, -0.78], + "I": [-0.91, 0.83, -0.25], + "K": [-0.51, 0.08, 0.6], + "L": [-0.74, 0.72, -0.16], + "M": [-0.7, 1, -0.32], + "N": [0.14, 0.2, -0.66], + "P": [-0.43, 0.73, -0.6], + "Q": [0.3, 1, -0.3], + "R": [-0.22, 0.27, 1], + "S": [-0.8, 0.61, -1], + "T": [-0.58, 0.85, -0.89], + "V": [-1, 0.79, -0.58], + "W": [1, 0.98, -0.47], + "Y": [0.97, 0.66, -0.16], + }, + "pepcats": { + "A": [1, 0, 0, 0, 0, 0], + "C": [1, 0, 1, 1, 0, 0], + "D": [0, 0, 1, 0, 0, 1], + "E": [0, 0, 1, 0, 0, 1], + "F": [1, 1, 0, 0, 0, 0], + "G": [0, 0, 0, 0, 0, 0], + "H": [1, 1, 0, 1, 1, 0], + "I": [1, 0, 0, 0, 0, 0], + "K": [1, 0, 0, 1, 1, 0], + "L": [1, 0, 0, 0, 0, 0], + "M": [1, 0, 1, 0, 0, 0], + "N": [0, 0, 1, 1, 0, 0], + "P": [1, 0, 0, 0, 0, 0], + "Q": [0, 0, 1, 1, 0, 0], + "R": [1, 0, 0, 1, 1, 0], + "S": [0, 0, 1, 1, 0, 0], + "T": [0, 0, 1, 1, 0, 0], + "V": [1, 0, 0, 0, 0, 0], + "W": [1, 1, 0, 1, 0, 0], + "Y": [1, 1, 1, 1, 0, 0], + }, + "peparc": { + "A": [1, 0, 0, 0, 0], + "C": [0, 1, 0, 0, 0], + "D": [0, 1, 0, 1, 0], + "E": [0, 1, 0, 1, 0], + "F": [1, 0, 0, 0, 0], + "G": [0, 0, 0, 0, 0], + "H": [0, 1, 1, 0, 0], + "I": [1, 0, 0, 0, 0], + "K": [0, 1, 1, 0, 0], + "L": [1, 0, 0, 0, 0], + "M": [1, 0, 0, 0, 0], + "N": [0, 1, 0, 0, 0], + "P": [0, 0, 0, 0, 1], + "Q": [0, 1, 0, 0, 0], + "R": [0, 1, 1, 0, 0], + "S": [0, 1, 0, 0, 0], + "T": [0, 1, 0, 0, 0], + "V": [1, 0, 0, 0, 0], + "W": [1, 0, 0, 0, 0], + "Y": [1, 0, 0, 0, 0], + }, + "polarity": { + "A": [0.395], + "C": [0.074], + "D": [1.0], + "E": [0.914], + "F": [0.037], + "G": [0.506], + "H": [0.679], + "I": [0.037], + "K": [0.79], + "L": [0.0], + "M": [0.099], + "N": [0.827], + "P": [0.383], + "Q": [0.691], + "R": [0.691], + "S": [0.531], + "T": [0.457], + "V": [0.123], + "W": [0.062], + "Y": [0.16], + }, + "ppcali": { + "A": [ + 0.070781, + 0.036271, + 2.042, + 0.083272, + 0.69089, + 0.15948, + -0.80893, + 0.24698, + 0.86525, + 0.68563, + -0.24665, + 0.61314, + -0.53343, + -0.50878, + -1.3646, + 2.2679, + -1.5644, + -0.75043, + -0.65875, + ], + "C": [ + 0.61013, + -0.93043, + -0.85983, + -2.2704, + 1.5877, + -2.0066, + -0.30314, + 1.2544, + -0.2832, + -1.2844, + -0.73449, + -0.11235, + -0.41152, + -0.0050164, + 0.28307, + 0.20522, + -0.021084, + -0.15627, + -0.32689, + ], + "D": [ + -1.3215, + 0.24063, + -0.032754, + -0.37863, + 1.2051, + 1.0001, + 2.1827, + 0.19212, + -0.60529, + 0.37639, + -0.46451, + -0.46788, + 1.4077, + -2.1661, + 0.72604, + -0.12332, + -0.8243, + -0.082989, + 0.053476, + ], + "E": [ + -0.87713, + 1.4905, + 1.0755, + 0.35944, + 1.567, + 0.41365, + 1.0944, + 0.72634, + -0.74957, + 0.038939, + 0.075057, + 0.78637, + -1.4543, + 1.6667, + -0.097439, + -0.24293, + 1.7687, + 0.36174, + -0.11585, + ], + "F": [ 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0.10206, + 0.07671, + -0.081968, + 0.20348, + ], + "Q": [ + -0.57747, + 0.97452, + -0.077547, + -0.0033488, + 0.17184, + -0.52537, + -0.27362, + -0.1366, + 0.2057, + -0.013066, + 1.8834, + -1.2736, + -0.84991, + 1.0445, + 0.69027, + -1.2866, + -2.6776, + 0.1683, + 0.086105, + ], + "R": [ + -0.62245, + 1.545, + -0.61966, + 0.19057, + -1.7485, + -1.3909, + -0.47526, + 1.3938, + -0.84556, + 1.7344, + -1.6516, + -0.52678, + 0.6791, + 0.24374, + -0.62551, + -0.0028271, + -0.053884, + 0.14926, + -0.17232, + ], + "S": [ + -0.86409, + -0.77147, + 0.38542, + -0.59389, + -0.53313, + -0.47585, + 0.31966, + -0.89716, + 1.8029, + 0.26431, + -0.23173, + -0.37626, + -0.47349, + -0.42878, + -0.47297, + -0.079826, + 0.57043, + 3.2057, + -0.18413, + ], + "T": [ + -0.33027, + -0.57447, + 0.18653, + -0.28941, + -0.62681, + -1.0737, + 0.80363, + -0.59525, + 1.8786, + 1.3971, + 0.63929, + 0.21281, + -0.067048, + 0.096271, + 1.323, + -0.36173, + 1.2261, + -2.2771, + -0.65412, + ], + "V": [ + 1.1675, + -0.61554, + 0.95405, + 0.11662, + -0.74473, + -1.1482, + 1.1309, + 0.12079, + -0.77171, + 0.18597, + 0.93442, + 1.201, + 0.3826, + -0.091573, + -0.31269, + 0.074367, + -0.22946, + 0.24322, + 2.9836, + ], + "W": [ + 1.1881, + 0.43789, + -1.7915, + 0.138, + 0.43088, + 1.6467, + -0.11987, + 1.7369, + 2.0818, + 0.33122, + 0.31829, + 1.1586, + 0.67649, + 0.30819, + -0.55772, + -0.54491, + -0.17969, + 0.24477, + 0.38674, + ], + "Y": [ + 0.54671, + -0.1468, + -1.5688, + 0.19001, + -1.2736, + 0.66162, + 1.1614, + -0.18614, + -0.70654, + -0.43634, + 0.44775, + -0.71366, + -2.5907, + -1.1649, + -1.1576, + 0.66572, + 0.21019, + -0.61016, + -0.34844, + ], + }, + "refractivity": { + "A": [0.102045615], + "C": [0.841053374], + "D": [0.282153774], + "E": [0.405831178], + "F": [0.691276746], + "G": [0], + "H": [0.512814484], + "I": [0.448154244], + "K": [0.50058782], + "L": [0.441570656], + "M": [0.508817305], + "N": [0.282153774], + "P": [0.256995062], + "Q": [0.405831178], + "R": [0.626851634], + "S": [0.149306372], + "T": [0.258876087], + "V": [0.327298378], + "W": [1], + "Y": [0.741359041], + }, + "t_scale": { + "A": [-8.4, -8.01, -3.73, -3.65, -6.12, -1.59, 1.56], + "C": [-2.44, -1.96, 0.93, -2.35, 1.31, 2.29, -1.52], + "D": [-6.84, -0.94, 17.68, -0.03, 3.44, 9.07, 4.32], + "E": [-6.5, 16.2, 17.28, 3.11, -4.75, -2.54, 4.72], + "F": [21.59, -5.73, 1.03, -3.3, 2.64, -5.02, 1.7], + "G": [-8.48, -10.37, -5.14, -6.51, -11.84, -3.6, 2.01], + "H": [15.28, -3.67, 6.72, -6.38, 4.12, -1.55, -2.85], + "I": [-2.97, 4.64, -0.77, 11, 3.26, -4.36, -7.88], + "K": [2.7, 13.46, -14.03, -2.55, 2.77, 0.15, 3.19], + "L": [2.61, 5.96, 1.97, 2.59, -4.77, -4.84, -5.44], + "M": [3.38, 12.43, -4.77, 0.45, -1.55, -0.6, 3.26], + "N": [-3.11, -1.22, 6.26, -9.38, 9.94, 7.66, -4.81], + "P": [-5.35, -9.07, -1.52, -8.79, -8.73, 4.29, -9.91], + "Q": [-5.31, 15.64, 8.44, 1.03, -4.32, -4.4, -0.52], + "R": [-2.27, 18.9, -18.24, -3.47, 3.03, 6.64, 0.45], + "S": [-15.88, -11.21, -2.44, -3.61, 3.46, -0.37, 8.98], + "T": [-17.81, -13.64, -5.19, 10.57, 6.91, -4.43, 3.49], + "V": [-5.8, -6.15, -2.26, 9.87, 5.28, -1.49, -7.54], + "W": [21.68, -8.78, -2.53, 15.53, -8.15, 11.98, 3.23], + "Y": [23.9, -6.47, 0.31, -4.14, 4.08, -7.28, 3.59], + }, + "tm_tend": { + "A": [0.38], + "C": [-0.3], + "D": [-3.27], + "E": [-2.9], + "F": [1.98], + "G": [-0.19], + "H": [-1.44], + "I": [1.97], + "K": [-3.46], + "L": [1.82], + "M": [1.4], + "N": [-1.62], + "P": [-1.44], + "Q": [-1.84], + "R": [-2.57], + "S": [-0.53], + "T": [-0.32], + "V": [1.46], + "W": [1.53], + "Y": [0.49], + }, + "z3": { + "A": [0.07, -1.73, 0.09], + "C": [0.71, -0.97, 4.13], + "D": [3.64, 1.13, 2.36], + "E": [3.08, 0.39, -0.07], + "F": [-4.92, 1.3, 0.45], + "G": [2.23, -5.36, 0.3], + "H": [2.41, 1.74, 1.11], + "I": [-4.44, -1.68, -1.03], + "K": [2.84, 1.41, -3.14], + "L": [-4.19, -1.03, -0.98], + "M": [-2.49, -0.27, -0.41], + "N": [3.22, 1.45, 0.84], + "P": [-1.22, 0.88, 2.23], + "Q": [2.18, 0.53, -1.14], + "R": [2.88, 2.52, -3.44], + "S": [1.96, -1.63, 0.57], + "T": [0.92, -2.09, -1.4], + "V": [-2.69, -2.53, -1.29], + "W": [-4.75, 3.65, 0.85], + "Y": [-1.39, 2.32, 0.01], + }, + "z5": { + "A": [0.24, -2.32, 0.6, -0.14, 1.3], + "C": [0.84, -1.67, 3.71, 0.18, -2.65], + "D": [3.98, 0.93, 1.93, -2.46, 0.75], + "E": [3.11, 0.26, -0.11, -3.04, -0.25], + "F": [-4.22, 1.94, 1.06, 0.54, -0.62], + "G": [2.05, -4.06, 0.36, -0.82, -0.38], + "H": [2.47, 1.95, 0.26, 3.9, 0.09], + "I": [-3.89, -1.73, -1.71, -0.84, 0.26], + "K": [2.29, 0.89, -2.49, 1.49, 0.31], + "L": [-4.28, -1.3, -1.49, -0.72, 0.84], + "M": [-2.85, -0.22, 0.47, 1.94, -0.98], + "N": [3.05, 1.62, 1.04, -1.15, 1.61], + "P": [-1.66, 0.27, 1.84, 0.7, 2], + "Q": [1.75, 0.5, -1.44, -1.34, 0.66], + "R": [3.52, 2.5, -3.5, 1.99, -0.17], + "S": [2.39, -1.07, 1.15, -1.39, 0.67], + "T": [0.75, -2.18, -1.12, -1.46, -0.4], + "V": [-2.59, -2.64, -1.54, -0.85, -0.02], + "W": [-4.36, 3.94, 0.59, 3.44, -1.59], + "Y": [-2.54, 2.44, 0.43, 0.04, -1.47], + }, + } + if scalename == "all": + d = { + "I": [], + "F": [], + "V": [], + "L": [], + "W": [], + "M": [], + "A": [], + "G": [], + "C": [], + "Y": [], + "P": [], + "T": [], + "S": [], + "H": [], + "E": [], + "N": [], + "Q": [], + "D": [], + "K": [], + "R": [], + } + for scale in scales.keys(): + for k, v in scales[scale].items(): + d[k].extend(v) + return "all", d + + elif scalename == "instability": + d = { + "A": { + "A": 1.0, + "C": 44.94, + "E": 1.0, + "D": -7.49, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": -7.49, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 1.0, + "P": 20.26, + "S": 1.0, + "R": 1.0, + "T": 1.0, + "W": 1.0, + "V": 1.0, + "Y": 1.0, + }, + "C": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 20.26, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 33.6, + "K": 1.0, + "M": 33.6, + "L": 20.26, + "N": 1.0, + "Q": -6.54, + "P": 20.26, + "S": 1.0, + "R": 1.0, + "T": 33.6, + "W": 24.68, + "V": -6.54, + "Y": 1.0, + }, + "E": { + "A": 1.0, + "C": 44.94, + "E": 33.6, + "D": 20.26, + "G": 1.0, + "F": 1.0, + "I": 20.26, + "H": -6.54, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 20.26, + "P": 20.26, + "S": 20.26, + "R": 1.0, + "T": 1.0, + "W": -14.03, + "V": 1.0, + "Y": 1.0, + }, + "D": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": 1.0, + "F": -6.54, + "I": 1.0, + "H": 1.0, + "K": -7.49, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 1.0, + "P": 1.0, + "S": 20.26, + "R": -6.54, + "T": -14.03, + "W": 1.0, + "V": 1.0, + "Y": 1.0, + }, + "G": { + "A": -7.49, + "C": 1.0, + "E": -6.54, + "D": 1.0, + "G": 13.34, + "F": 1.0, + "I": -7.49, + "H": 1.0, + "K": -7.49, + "M": 1.0, + "L": 1.0, + "N": -7.49, + "Q": 1.0, + "P": 1.0, + "S": 1.0, + "R": 1.0, + "T": -7.49, + "W": 13.34, + "V": 1.0, + "Y": -7.49, + }, + "F": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 13.34, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 1.0, + "K": -14.03, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 1.0, + "P": 20.26, + "S": 1.0, + "R": 1.0, + "T": 1.0, + "W": 1.0, + "V": 1.0, + "Y": 33.601, + }, + "I": { + "A": 1.0, + "C": 1.0, + "E": 44.94, + "D": 1.0, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 13.34, + "K": -7.49, + "M": 1.0, + "L": 20.26, + "N": 1.0, + "Q": 1.0, + "P": -1.88, + "S": 1.0, + "R": 1.0, + "T": 1.0, + "W": 1.0, + "V": -7.49, + "Y": 1.0, + }, + "H": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": -9.37, + "F": -9.37, + "I": 44.94, + "H": 1.0, + "K": 24.68, + "M": 1.0, + "L": 1.0, + "N": 24.68, + "Q": 1.0, + "P": -1.88, + "S": 1.0, + "R": 1.0, + "T": -6.54, + "W": -1.88, + "V": 1.0, + "Y": 44.94, + }, + "K": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": -7.49, + "F": 1.0, + "I": -7.49, + "H": 1.0, + "K": 1.0, + "M": 33.6, + "L": -7.49, + "N": 1.0, + "Q": 24.64, + "P": -6.54, + "S": 1.0, + "R": 33.6, + "T": 1.0, + "W": 1.0, + "V": -7.49, + "Y": 1.0, + }, + "M": { + "A": 13.34, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 58.28, + "K": 1.0, + "M": -1.88, + "L": 1.0, + "N": 1.0, + "Q": -6.54, + "P": 44.94, + "S": 44.94, + "R": -6.54, + "T": -1.88, + "W": 1.0, + "V": 1.0, + "Y": 24.68, + }, + "L": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 1.0, + "K": -7.49, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 33.6, + "P": 20.26, + "S": 1.0, + "R": 20.26, + "T": 1.0, + "W": 24.68, + "V": 1.0, + "Y": 1.0, + }, + "N": { + "A": 1.0, + "C": -1.88, + "E": 1.0, + "D": 1.0, + "G": -14.03, + "F": -14.03, + "I": 44.94, + "H": 1.0, + "K": 24.68, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": -6.54, + "P": -1.88, + "S": 1.0, + "R": 1.0, + "T": -7.49, + "W": -9.37, + "V": 1.0, + "Y": 1.0, + }, + "Q": { + "A": 1.0, + "C": -6.54, + "E": 20.26, + "D": 20.26, + "G": 1.0, + "F": -6.54, + "I": 1.0, + "H": 1.0, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 20.26, + "P": 20.26, + "S": 44.94, + "R": 1.0, + "T": 1.0, + "W": 1.0, + "V": -6.54, + "Y": -6.54, + }, + "P": { + "A": 20.26, + "C": -6.54, + "E": 18.38, + "D": -6.54, + "G": 1.0, + "F": 20.26, + "I": 1.0, + "H": 1.0, + "K": 1.0, + "M": -6.54, + "L": 1.0, + "N": 1.0, + "Q": 20.26, + "P": 20.26, + "S": 20.26, + "R": -6.54, + "T": 1.0, + "W": -1.88, + "V": 20.26, + "Y": 1.0, + }, + "S": { + "A": 1.0, + "C": 33.6, + "E": 20.26, + "D": 1.0, + "G": 1.0, + "F": 1.0, + "I": 1.0, + "H": 1.0, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 20.26, + "P": 44.94, + "S": 20.26, + "R": 20.26, + "T": 1.0, + "W": 1.0, + "V": 1.0, + "Y": 1.0, + }, + "R": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": -7.49, + "F": 1.0, + "I": 1.0, + "H": 20.26, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": 13.34, + "Q": 20.26, + "P": 20.26, + "S": 44.94, + "R": 58.28, + "T": 1.0, + "W": 58.28, + "V": 1.0, + "Y": -6.54, + }, + "T": { + "A": 1.0, + "C": 1.0, + "E": 20.26, + "D": 1.0, + "G": -7.49, + "F": 13.34, + "I": 1.0, + "H": 1.0, + "K": 1.0, + "M": 1.0, + "L": 1.0, + "N": -14.03, + "Q": -6.54, + "P": 1.0, + "S": 1.0, + "R": 1.0, + "T": 1.0, + "W": -14.03, + "V": 1.0, + "Y": 1.0, + }, + "W": { + "A": -14.03, + "C": 1.0, + "E": 1.0, + "D": 1.0, + "G": -9.37, + "F": 1.0, + "I": 1.0, + "H": 24.68, + "K": 1.0, + "M": 24.68, + "L": 13.34, + "N": 13.34, + "Q": 1.0, + "P": 1.0, + "S": 1.0, + "R": 1.0, + "T": -14.03, + "W": 1.0, + "V": -7.49, + "Y": 1.0, + }, + "V": { + "A": 1.0, + "C": 1.0, + "E": 1.0, + "D": -14.03, + "G": -7.49, + "F": 1.0, + "I": 1.0, + "H": 1.0, + "K": -1.88, + "M": 1.0, + "L": 1.0, + "N": 1.0, + "Q": 1.0, + "P": 20.26, + "S": 1.0, + "R": 1.0, + "T": -7.49, + "W": 1.0, + "V": 1.0, + "Y": -6.54, + }, + "Y": { + "A": 24.68, + "C": 1.0, + "E": -6.54, + "D": 24.68, + "G": -7.49, + "F": 1.0, + "I": 1.0, + "H": 13.34, + "K": 1.0, + "M": 44.94, + "L": 1.0, + "N": 1.0, + "Q": 1.0, + "P": 13.34, + "S": 1.0, + "R": -15.91, + "T": -7.49, + "W": -9.37, + "V": 1.0, + "Y": 13.34, + }, + } + return "instability", d + + else: + return scalename, scales[scalename] + + +def read_fasta(inputfile): + """Method for loading sequences from a FASTA formatted file into :py:attr:`sequences` & :py:attr:`names`. + This method is used by the base class :class:`modlamp.descriptors.PeptideDescriptor` if the input is a FASTA file. + + :param inputfile: .fasta file with sequences and headers to read + :return: list of sequences in the attribute :py:attr:`sequences` with corresponding sequence names in + :py:attr:`names`. + """ + names = list() # list for storing names + sequences = list() # list for storing sequences + seq = str() + with open(inputfile) as f: + all = f.readlines() + last = all[-1] + for line in all: + if line.startswith(">"): + names.append( + line.split(" ")[0][1:].strip() + ) # add FASTA name without description as molecule name + sequences.append(seq.strip()) + seq = str() + elif line == last: + seq += line.strip() # remove potential white space + sequences.append(seq.strip()) + else: + seq += line.strip() # remove potential white space + return sequences[1:], names + + +def save_fasta(filename, sequences, names=None): + """Method for saving sequences in the instance :py:attr:`sequences` to a file in FASTA format. + + :param filename: {str} output filename (ending .fasta) + :param sequences: {list} sequences to be saved to file + :param names: {list} whether sequence names from self.names should be saved as sequence identifiers + :return: a FASTA formatted file containing the generated sequences + """ + if os.path.exists(filename): + os.remove(filename) # remove outputfile, it it exists + + with open(filename, "w") as o: + for n, seq in enumerate(sequences): + if names: + o.write(">" + str(names[n]) + "\n") + else: + o.write(">Seq_" + str(n) + "\n") + o.write(seq + "\n") + + +def aa_weights(): + """Function holding molecular weight data on all natural amino acids. + + :return: dictionary with amino acid letters and corresponding weights + + .. versionadded:: v2.4.1 + """ + weights = { + "A": 89.093, + "C": 121.158, + "D": 133.103, + "E": 147.129, + "F": 165.189, + "G": 75.067, + "H": 155.155, + "I": 131.173, + "K": 146.188, + "L": 131.173, + "M": 149.211, + "N": 132.118, + "P": 115.131, + "Q": 146.145, + "R": 174.20, + "S": 105.093, + "T": 119.119, + "V": 117.146, + "W": 204.225, + "Y": 181.189, + } + return weights + + +def count_aas(seq, scale="relative"): + """Function to count the amino acids occuring in a given sequence. + + :param seq: {str} amino acid sequence + :param scale: {'absolute' or 'relative'} defines whether counts or frequencies are given for each AA + :return: {dict} dictionary with amino acids as keys and their counts in the sequence as values. + """ + if seq == "": # error if len(seq) == 0 + seq = " " + aas = [ + "A", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "M", + "N", + "P", + "Q", + "R", + "S", + "T", + "V", + "W", + "Y", + ] + scl = 1.0 + if scale == "relative": + scl = len(seq) + aa = {a: (float(seq.count(a)) / scl) for a in aas} + aa = collections.OrderedDict(sorted(list(aa.items()))) + return aa + + +def count_ngrams(seq, n): + """Function to count the n-grams of an amino acid sequence. N can be one integer or a list of integers + + :param seq: {str} amino acid sequence + :param n: {int or list of ints} defines whether counts or frequencies are given for each AA + :return: {dict} dictionary with n-grams as keys and their counts in the sequence as values. + """ + if seq == "": + seq = " " + if isinstance(n, int): + n = [n] + ngrams = list() + for i in n: + ngrams.extend([seq[j : j + i] for j in range(len(seq) - (i - 1))]) + counts = {g: (seq.count(g)) for g in set(ngrams)} + counts = collections.OrderedDict( + sorted(counts.items(), key=operator.itemgetter(1), reverse=True) + ) + return counts + + +def aa_energies(): + """Function holding free energies of transfer between cyclohexane and water for all natural amino acids. + H. G. Boman, D. Wade, I. a Boman, B. Wåhlin, R. B. Merrifield, *FEBS Lett*. **1989**, *259*, 103–106. + + :return: dictionary with amino acid letters and corresponding energies. + """ + energies = { + "L": -4.92, + "I": -4.92, + "V": -4.04, + "F": -2.98, + "M": -2.35, + "W": -2.33, + "A": -1.81, + "C": -1.28, + "G": -0.94, + "Y": 0.14, + "T": 2.57, + "S": 3.40, + "H": 4.66, + "Q": 5.54, + "K": 5.55, + "N": 6.64, + "E": 6.81, + "D": 8.72, + "R": 14.92, + "P": 0.0, + } + return energies + + +def ngrams_apd(): + """Function returning the most frequent 2-, 3- and 4-grams from all sequences in the `APD3 + <http://aps.unmc.edu/AP/>`_, version August 2016 with 2727 sequences. + For all 2, 3 and 4grams, all possible ngrams were generated from all sequences and the top 50 most frequent + assembled into a list. Finally, leading and tailing spaces were striped and duplicates as well as ngrams containing + spaces were removed. + + :return: numpy.array containing most frequent ngrams + """ + return np.array( + [ + "AGK", + "CKI", + "RR", + "YGGG", + "LSGL", + "RG", + "YGGY", + "PRP", + "LGGG", + "GV", + "GT", + "GS", + "GR", + "IAG", + "GG", + "GF", + "GC", + "GGYG", + "GA", + "GL", + "GK", + "GI", + "IPC", + "KAA", + "LAK", + "GLGG", + "GGLG", + "CKIT", + "GAGK", + "LLSG", + "LKK", + "FLP", + "LSG", + "SCK", + "LLS", + "GETC", + "VLG", + "GKLL", + "LLG", + "C", + "KCKI", + "G", + "VGK", + "CSC", + "TKKC", + "GCS", + "GKA", + "IGK", + "GESC", + "KVCY", + "KKL", + "KKI", + "KKC", + "LGGL", + "GLL", + "CGE", + "GGYC", + "GLLS", + "GLF", + "AKK", + "GKAA", + "ESCV", + "GLP", + "CGES", + "PCGE", + "FL", + "CGET", + "GLW", + "KGAA", + "KAAL", + "GGY", + "GGG", + "IKG", + "LKG", + "GGL", + "CK", + "GTC", + "CG", + "SKKC", + "CS", + "CR", + "KC", + "AGKA", + "KA", + "KG", + "LKCK", + "SCKL", + "KK", + "KI", + "KN", + "KL", + "SK", + "KV", + "SL", + "SC", + "SG", + "AAA", + "VAK", + "AAL", + "AAK", + "GGGG", + "KNVA", + "GGGL", + "GYG", + "LG", + "LA", + "LL", + "LK", + "LS", + "LP", + "GCSC", + "TC", + "GAA", + "AA", + "VA", + "VC", + "AG", + "VG", + "AI", + "AK", + "VL", + "AL", + "TPGC", + "IK", + "IA", + "IG", + "YGG", + "LGK", + "CSCK", + "GYGG", + "LGG", + "KGA", + ] + ) + + +def aa_formulas(): + """ + Function returning the molecular formulas of all amino acids. All amino acids are considered in the neutral form + (uncharged). + """ + formulas = { + "A": {"C": 3, "H": 7, "N": 1, "O": 2, "S": 0}, + "C": {"C": 3, "H": 7, "N": 1, "O": 2, "S": 1}, + "D": {"C": 4, "H": 7, "N": 1, "O": 4, "S": 0}, + "E": {"C": 5, "H": 9, "N": 1, "O": 4, "S": 0}, + "F": {"C": 9, "H": 11, "N": 1, "O": 2, "S": 0}, + "G": {"C": 2, "H": 5, "N": 1, "O": 2, "S": 0}, + "H": {"C": 6, "H": 9, "N": 3, "O": 2, "S": 0}, + "I": {"C": 6, "H": 13, "N": 1, "O": 2, "S": 0}, + "K": {"C": 6, "H": 14, "N": 2, "O": 2, "S": 0}, + "L": {"C": 6, "H": 13, "N": 1, "O": 2, "S": 0}, + "M": {"C": 5, "H": 11, "N": 1, "O": 2, "S": 1}, + "N": {"C": 4, "H": 8, "N": 2, "O": 3, "S": 0}, + "P": {"C": 5, "H": 9, "N": 1, "O": 2, "S": 0}, + "Q": {"C": 5, "H": 10, "N": 2, "O": 3, "S": 0}, + "R": {"C": 6, "H": 14, "N": 4, "O": 2, "S": 0}, + "S": {"C": 3, "H": 7, "N": 1, "O": 3, "S": 0}, + "T": {"C": 4, "H": 9, "N": 1, "O": 3, "S": 0}, + "V": {"C": 5, "H": 11, "N": 1, "O": 2, "S": 0}, + "W": {"C": 11, "H": 12, "N": 2, "O": 2, "S": 0}, + "Y": {"C": 9, "H": 11, "N": 1, "O": 3, "S": 0}, + } + return formulas
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotWheels/descriptors.py Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,1183 @@ +# -*- coding: utf-8 -*- +""" +.. currentmodule:: modlamp.descriptors + +.. moduleauthor:: modlab Alex Mueller ETH Zurich <alex.mueller@pharma.ethz.ch> + +This module incorporates different classes to calculate peptide descriptor values. The following classes are available: + +============================= ============================================================================ +Class Characteristics +============================= ============================================================================ +:py:class:`GlobalDescriptor` Global one-dimensional peptide descriptors calculated from the AA sequence. +:py:class:`PeptideDescriptor` AA scale based global or convoluted descriptors (auto-/cross-correlated). +============================= ============================================================================ + +.. seealso:: :class:`modlamp.core.BaseDescriptor` from which the classes in :mod:`modlamp.descriptors` inherit. +""" + +import sys + +import numpy as np +from scipy import stats +from sklearn.externals.joblib import Parallel, delayed + +from plotWheels.core import ( + BaseDescriptor, + load_scale, + count_aas, + aa_weights, + aa_energies, + aa_formulas, +) + +__author__ = "Alex Müller, Gisela Gabernet" +__docformat__ = "restructuredtext en" + + +def _one_autocorr(seq, window, scale): + """Private function used for calculating auto-correlated descriptors for 1 given sequence, window and an AA scale. + This function is used by the :py:func:`calculate_autocorr` method of :py:class:`PeptideDescriptor`. + + :param seq: {str} amino acid sequence to calculate descriptor for + :param window: {int} correlation-window size + :param scale: {str} amino acid scale to be used to calculate descriptor + :return: {numpy.array} calculated descriptor data + """ + try: + m = list() # list of lists to store translated sequence values + for l in range(len(seq)): # translate AA sequence into values + m.append(scale[str(seq[l])]) + # auto-correlation in defined sequence window + seqdesc = list() + for dist in range(window): # for all correlation distances + for val in range( + len(scale["A"]) + ): # for all features of the descriptor scale + valsum = list() + cntr = 0.0 + for pos in range(len(seq)): # for every position in the sequence + if (pos + dist) < len( + seq + ): # check if corr distance is possible at that sequence position + cntr += 1 # counter to scale sum + valsum.append(m[pos][val] * m[pos + dist][val]) + seqdesc.append( + sum(valsum) / cntr + ) # append scaled correlation distance values + return seqdesc + except ZeroDivisionError: + print( + "ERROR!\nThe chosen correlation window % i is larger than the sequence %s !" + % (window, seq) + ) + + +def _one_crosscorr(seq, window, scale): + """Private function used for calculating cross-correlated descriptors for 1 given sequence, window and an AA scale. + This function is used by the :py:func:`calculate_crosscorr` method of :py:class:`PeptideDescriptor`. + + :param seq: {str} amino acid sequence to calculate descriptor for + :param window: {int} correlation-window size + :param scale: {str} amino acid scale to be used to calculate descriptor + :return: {numpy.array} calculated descriptor data + """ + try: + m = list() # list of lists to store translated sequence values + for l in range(len(seq)): # translate AA sequence into values + m.append(scale[str(seq[l])]) + # auto-correlation in defined sequence window + seqdesc = list() + for val in range(len(scale["A"])): # for all features of the descriptor scale + for cc in range(len(scale["A"])): # for every feature cross correlation + if (val + cc) < len( + scale["A"] + ): # check if corr distance is in range of the num of features + for dist in range(window): # for all correlation distances + cntr = float() + valsum = list() + for pos in range( + len(seq) + ): # for every position in the sequence + if (pos + dist) < len( + seq + ): # check if corr distance is possible at that sequence pos + cntr += 1 # counter to scale sum + valsum.append(m[pos][val] * m[pos + dist][val + cc]) + seqdesc.append( + sum(valsum) / cntr + ) # append scaled correlation distance values + return seqdesc + except ZeroDivisionError: + print( + "ERROR!\nThe chosen correlation window % i is larger than the sequence %s !" + % (window, seq) + ) + + +def _one_arc(seq, modality, scale): + """Privat function used for calculating arc descriptors for one sequence and AA scale. This function is used by + :py:func:`calculate_arc` method method of :py:class:`PeptideDescriptor`. + + :param seq: {str} amino acid sequence to calculate descriptor for + :param scale: {str} amino acid scale to be used to calculate descriptor + :return: {numpy.array} calculated descriptor data + """ + desc_mat = [] + for aa in seq: + desc_mat.append(scale[aa]) + desc_mat = np.asarray(desc_mat) + + # Check descriptor dimension + desc_dim = desc_mat.shape[1] + + # list to store descriptor values for all windows + allwindows_arc = [] + + if len(seq) > 18: + window = 18 + # calculates number of windows in sequence + num_windows = len(seq) - window + else: + window = len(seq) + num_windows = 1 + + # loop through all windows + for j in range(num_windows): + # slices descriptor matrix into current window + window_mat = desc_mat[j : j + window, :] + + # defines order of amino acids in helical projection + order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7] + + # orders window descriptor matrix into helical projection order + ordered = [] + for pos in order: + try: + ordered.append(window_mat[pos, :]) + except: + # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions + ordered.append([2] * desc_dim) + ordered = np.asarray(ordered) + + window_arc = [] + + # loop through pharmacophoric features + for m in range(desc_dim): + all_arcs = ( + [] + ) # stores all arcs that can be found of a pharmacophoric feature + arc = 0 + + for n in range( + 18 + ): # for all positions in helix, regardless of sequence length + if ( + ordered[n, m] == 0 + ): # if position does not contain pharmacophoric feature + all_arcs.append(arc) # append previous arc to all arcs list + arc = 0 # arc is initialized + elif ( + ordered[n, m] == 1 + ): # if position contains pharmacophoric feature(PF), elongate arc by 20° + arc += 20 + elif ordered[n, m] == 2: # if position doesn't contain amino acid: + if ( + ordered[n - 1, m] == 1 + ): # if previous position contained PF add 10° + arc += 10 + elif ( + ordered[n - 1, m] == 0 + ): # if previous position didn't contain PF don't add anything + arc += 0 + elif ( + ordered[n - 2, m] == 1 + ): # if previous position is empty then check second previous for PF + arc += 10 + if ( + n == 17 + ): # if we are at the last position check for position n=0 instead of next position. + if ordered[0, m] == 1: # if it contains PF add 10° extra + arc += 10 + else: # if next position contains PF add 10° extra + if ordered[n + 1, m] == 1: + arc += 10 + elif ordered[n + 1, m] == 0: + arc += 0 + else: # if next position is empty check for 2nd next position + if n == 16: + if ordered[0, m] == 1: + arc += 10 + else: + if ordered[n + 2, m] == 1: + arc += 10 + + all_arcs.append(arc) + if not arc == 360: + arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together + all_arcs = [arc0] + all_arcs[1:] + + window_arc.append( + np.max(all_arcs) + ) # append to window arcs the maximum arc of this PF + allwindows_arc.append(window_arc) # append all PF arcs of this window + + allwindows_arc = np.asarray(allwindows_arc) + + if modality == "max": + final_arc = np.max( + allwindows_arc, axis=0 + ) # calculate maximum / mean arc along all windows + elif modality == "mean": + final_arc = np.mean(allwindows_arc, axis=0) + else: + print('modality is unknown, please choose between "max" and "mean"\n.') + sys.exit() + return final_arc + + +def _charge(seq, ph=7.0, amide=False): + """Calculates charge of a single sequence. The method used is first described by Bjellqvist. In the case of + amidation, the value for the 'Cterm' pKa is 15 (and Cterm is added to the pos_pks dictionary. + The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). + + **pos_pks** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} + + **neg_pks** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} + + :param ph: {float} pH at which to calculate peptide charge. + :param amide: {boolean} whether the sequences have an amidated C-terminus. + :return: {array} descriptor values in the attribute :py:attr:`descriptor + """ + + if amide: + pos_pks = {"Nterm": 9.38, "K": 10.67, "R": 12.10, "H": 6.04} + neg_pks = {"Cterm": 15.0, "D": 3.71, "E": 4.15, "C": 8.14, "Y": 10.10} + else: + pos_pks = {"Nterm": 9.38, "K": 10.67, "R": 12.10, "H": 6.04} + neg_pks = {"Cterm": 2.15, "D": 3.71, "E": 4.15, "C": 8.14, "Y": 10.10} + + aa_content = count_aas(seq, scale="absolute") + aa_content["Nterm"] = 1.0 + aa_content["Cterm"] = 1.0 + pos_charge = 0.0 + for aa, pK in pos_pks.items(): + c_r = 10 ** (pK - ph) + partial_charge = c_r / (c_r + 1.0) + pos_charge += aa_content[aa] * partial_charge + neg_charge = 0.0 + for aa, pK in neg_pks.items(): + c_r = 10 ** (ph - pK) + partial_charge = c_r / (c_r + 1.0) + neg_charge += aa_content[aa] * partial_charge + return round(pos_charge - neg_charge, 3) + + +class GlobalDescriptor(BaseDescriptor): + """ + Base class for global, non-amino acid scale dependant descriptors. The following descriptors can be calculated by + the **methods** linked below: + + - `Sequence Length <modlamp.html#modlamp.descriptors.GlobalDescriptor.length>`_ + - `Molecular Formula <modlamp.html#modlamp.descriptors.GlobalDescriptor.formula>`_ + - `Molecular Weight <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_MW>`_ + - `Sequence Charge <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_charge>`_ + - `Charge Density <modlamp.html#modlamp.descriptors.GlobalDescriptor.charge_density>`_ + - `Isoelectric Point <modlamp.html#modlamp.descriptors.GlobalDescriptor.isoelectric_point>`_ + - `Instability Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.instability_index>`_ + - `Aromaticity <modlamp.html#modlamp.descriptors.GlobalDescriptor.aromaticity>`_ + - `Aliphatic Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.aliphatic_index>`_ + - `Boman Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.boman_index>`_ + - `Hydrophobic Ratio <modlamp.html#modlamp.descriptors.GlobalDescriptor.hydrophobic_ratio>`_ + - `all of the above <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_all>`_ + """ + + def length(self, append=False): + """ + Method to calculate the length (total AA count) of every sequence in the attribute :py:attr:`sequences`. + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of sequence lengths in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor(['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR']) + >>> desc.length() + >>> desc.descriptor + array([[ 8.], [12.], [12.]]) + """ + desc = [] + for seq in self.sequences: + desc.append(float(len(seq.strip()))) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("Length") + else: + self.descriptor = np.array(desc) + self.featurenames = ["Length"] + + def formula(self, amide=False, append=False): + """Method to calculate the molecular formula of every sequence in the attribute :py:attr:`sequences`. + + :param amide: {boolean} whether the sequences are C-terminally amidated. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of molecular formulas {str} in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor(['KADSFLSADGHSADFSLDKKLKERL', 'ERTILSDFPQWWFASLDFLNC', 'ACDEFGHIKLMNPQRSTVWY']) + >>> desc.formula(amide=True) + >>> for v in desc.descriptor: + ... print(v[0]) + C122 H197 N35 O39 + C121 H168 N28 O33 S + C106 H157 N29 O30 S2 + + .. seealso:: :py:func:`modlamp.core.aa_formulas()` + + .. versionadded:: v2.7.6 + """ + desc = [] + formulas = aa_formulas() + for seq in self.sequences: + f = {"C": 0, "H": 0, "N": 0, "O": 0, "S": 0} + for aa in seq: # loop over all AAs + for k in f.keys(): + f[k] += formulas[aa][k] + + # substract H2O for every peptide bond + f["H"] -= 2 * (len(seq) - 1) + f["O"] -= len(seq) - 1 + + if amide: # add C-terminal amide --> replace OH with NH2 + f["O"] -= 1 + f["H"] += 1 + f["N"] += 1 + + if f["S"] != 0: + val = "C%s H%s N%s O%s %s%s" % ( + f["C"], + f["H"], + f["N"], + f["O"], + "S", + f["S"], + ) + else: + val = "C%s H%s N%s O%s" % (f["C"], f["H"], f["N"], f["O"]) + + desc.append([val]) + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("Formula") + else: + self.descriptor = np.array(desc) + self.featurenames = ["Formula"] + + def calculate_MW(self, amide=False, append=False): + """Method to calculate the molecular weight [g/mol] of every sequence in the attribute :py:attr:`sequences`. + + :param amide: {boolean} whether the sequences are C-terminally amidated (subtracts 0.95 from the MW). + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('IAESFKGHIPL') + >>> desc.calculate_MW(amide=True) + >>> desc.descriptor + array([[ 1210.43]]) + + .. seealso:: :py:func:`modlamp.core.aa_weights()` + + .. versionchanged:: v2.1.5 amide option added + """ + desc = [] + weights = aa_weights() + for seq in self.sequences: + mw = [] + for aa in seq: # sum over aa weights + mw.append(weights[aa]) + desc.append( + round(sum(mw) - 18.015 * (len(seq) - 1), 2) + ) # sum over AA MW and subtract H20 MW for every + # peptide bond + desc = np.asarray(desc).reshape(len(desc), 1) + if ( + amide + ): # if sequences are amidated, subtract 0.98 from calculated MW (OH - NH2) + desc = [d - 0.98 for d in desc] + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("MW") + else: + self.descriptor = np.array(desc) + self.featurenames = ["MW"] + + def calculate_charge(self, ph=7.0, amide=False, append=False): + """Method to overall charge of every sequence in the attribute :py:attr:`sequences`. + + The method used is first described by Bjellqvist. In the case of amidation, the value for the 'Cterm' pKa is 15 + (and Cterm is added to the pos_pKs dictionary. + The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). + + **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} + + **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} + + :param ph: {float} ph at which to calculate peptide charge. + :param amide: {boolean} whether the sequences have an amidated C-terminus. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('KLAKFGKRSELVALSG') + >>> desc.calculate_charge(ph=7.4, amide=True) + >>> desc.descriptor + array([[ 3.989]]) + """ + + desc = [] + for seq in self.sequences: + desc.append( + _charge(seq, ph, amide) + ) # calculate charge with helper function + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("Charge") + else: + self.descriptor = np.array(desc) + self.featurenames = ["Charge"] + + def charge_density(self, ph=7.0, amide=False, append=False): + """Method to calculate the charge density (charge / MW) of every sequences in the attributes :py:attr:`sequences` + + :param ph: {float} pH at which to calculate peptide charge. + :param amide: {boolean} whether the sequences have an amidated C-terminus. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor`. + :Example: + + >>> desc = GlobalDescriptor('GNSDLLIEQRTLLASDEF') + >>> desc.charge_density(ph=6, amide=True) + >>> desc.descriptor + array([[-0.00097119]]) + """ + self.calculate_charge(ph, amide) + charges = self.descriptor + self.calculate_MW(amide) + masses = self.descriptor + desc = charges / masses + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("ChargeDensity") + else: + self.descriptor = np.array(desc) + self.featurenames = ["ChargeDensity"] + + def isoelectric_point(self, amide=False, append=False): + """ + Method to calculate the isoelectric point of every sequence in the attribute :py:attr:`sequences`. + The pK scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed). + + **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04} + + **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10} + + :param amide: {boolean} whether the sequences have an amidated C-terminus. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('KLFDIKFGHIPQRST') + >>> desc.isoelectric_point() + >>> desc.descriptor + array([[ 10.6796875]]) + """ + ph, ph1, ph2 = float(), float(), float() + desc = [] + for seq in self.sequences: + + # Bracket between ph1 and ph2 + ph = 7.0 + charge = _charge(seq, ph, amide) + if charge > 0.0: + ph1 = ph + charge1 = charge + while charge1 > 0.0: + ph = ph1 + 1.0 + charge = _charge(seq, ph, amide) + if charge > 0.0: + ph1 = ph + charge1 = charge + else: + ph2 = ph + break + else: + ph2 = ph + charge2 = charge + while charge2 < 0.0: + ph = ph2 - 1.0 + charge = _charge(seq, ph, amide) + if charge < 0.0: + ph2 = ph + charge2 = charge + else: + ph1 = ph + break + # Bisection + while ph2 - ph1 > 0.0001 and charge != 0.0: + ph = (ph1 + ph2) / 2.0 + charge = _charge(seq, ph, amide) + if charge > 0.0: + ph1 = ph + else: + ph2 = ph + desc.append(ph) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("pI") + else: + self.descriptor = np.array(desc) + self.featurenames = ["pI"] + + def instability_index(self, append=False): + """ + Method to calculate the instability of every sequence in the attribute :py:attr:`sequences`. + The instability index is a prediction of protein stability based on the amino acid composition. + ([1] K. Guruprasad, B. V Reddy, M. W. Pandit, Protein Eng. 1990, 4, 155–161.) + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('LLASMNDLLAKRST') + >>> desc.instability_index() + >>> desc.descriptor + array([[ 63.95714286]]) + """ + + desc = [] + dimv = load_scale("instability")[1] + for seq in self.sequences: + stabindex = float() + for i in range(len(seq) - 1): + stabindex += dimv[seq[i]][seq[i + 1]] + desc.append((10.0 / len(seq)) * stabindex) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("InstabilityInd") + else: + self.descriptor = np.array(desc) + self.featurenames = ["InstabilityInd"] + + def aromaticity(self, append=False): + """ + Method to calculate the aromaticity of every sequence in the attribute :py:attr:`sequences`. + According to Lobry, 1994, it is simply the relative frequency of Phe+Trp+Tyr. + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('GLFYWRFFLQRRFLYWW') + >>> desc.aromaticity() + >>> desc.descriptor + array([[ 0.52941176]]) + """ + desc = [] + for seq in self.sequences: + f = seq.count("F") + w = seq.count("W") + y = seq.count("Y") + desc.append(float(f + w + y) / len(seq)) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("Aromaticity") + else: + self.descriptor = np.array(desc) + self.featurenames = ["Aromaticity"] + + def aliphatic_index(self, append=False): + """ + Method to calculate the aliphatic index of every sequence in the attribute :py:attr:`sequences`. + According to Ikai, 1980, the aliphatic index is a measure of thermal stability of proteins and is dependant + on the relative volume occupied by aliphatic amino acids (A,I,L & V). + ([1] A. Ikai, J. Biochem. 1980, 88, 1895–1898.) + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('KWLKYLKKLAKLVK') + >>> desc.aliphatic_index() + >>> desc.descriptor + array([[ 139.28571429]]) + """ + desc = [] + aa_dict = aa_weights() + for seq in self.sequences: + d = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa + d = { + k: (float(d[k]) / len(seq)) * 100 for k in d.keys() + } # get mole percent of all AA + desc.append( + d["A"] + 2.9 * d["V"] + 3.9 * (d["I"] + d["L"]) + ) # formula for calculating the AI (Ikai, 1980) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("AliphaticInd") + else: + self.descriptor = np.array(desc) + self.featurenames = ["AliphaticInd"] + + def boman_index(self, append=False): + """Method to calculate the boman index of every sequence in the attribute :py:attr:`sequences`. + According to Boman, 2003, the boman index is a measure for protein-protein interactions and is calculated by + summing over all amino acid free energy of transfer [kcal/mol] between water and cyclohexane,[2] followed by + dividing by sequence length. + ([1] H. G. Boman, D. Wade, I. a Boman, B. Wåhlin, R. B. Merrifield, *FEBS Lett*. **1989**, *259*, 103–106. + [2] A. Radzick, R. Wolfenden, *Biochemistry* **1988**, *27*, 1664–1670.) + + .. seealso:: :py:func:`modlamp.core.aa_energies()` + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('GLFDIVKKVVGALGSL') + >>> desc.boman_index() + >>> desc.descriptor + array([[-1.011875]]) + """ + d = aa_energies() + desc = [] + for seq in self.sequences: + val = [] + for a in seq: + val.append(d[a]) + desc.append(sum(val) / len(val)) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("BomanInd") + else: + self.descriptor = np.array(desc) + self.featurenames = ["BomanInd"] + + def hydrophobic_ratio(self, append=False): + """ + Method to calculate the hydrophobic ratio of every sequence in the attribute :py:attr:`sequences`, which is the + relative frequency of the amino acids **A,C,F,I,L,M & V**. + + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('VALLYWRTVLLAIII') + >>> desc.hydrophobic_ratio() + >>> desc.descriptor + array([[ 0.73333333]]) + """ + desc = [] + aa_dict = aa_weights() + for seq in self.sequences: + pa = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa + # formula for calculating the AI (Ikai, 1980): + desc.append( + (pa["A"] + pa["C"] + pa["F"] + pa["I"] + pa["L"] + pa["M"] + pa["V"]) + / float(len(seq)) + ) + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + self.featurenames.append("HydrophRatio") + else: + self.descriptor = np.array(desc) + self.featurenames = ["HydrophRatio"] + + def calculate_all(self, ph=7.4, amide=True): + """Method combining all global descriptors and appending them into the feature matrix in the attribute + :py:attr:`descriptor`. + + :param ph: {float} pH at which to calculate peptide charge + :param amide: {boolean} whether the sequences have an amidated C-terminus. + :return: array of descriptor values in the attribute :py:attr:`descriptor` + :Example: + + >>> desc = GlobalDescriptor('AFGHFKLKKLFIFGHERT') + >>> desc.calculate_all(amide=True) + >>> desc.featurenames + ['Length', 'MW', 'ChargeDensity', 'pI', 'InstabilityInd', 'Aromaticity', 'AliphaticInd', 'BomanInd', 'HydRatio'] + >>> desc.descriptor + array([[ 18., 2.17559000e+03, 1.87167619e-03, 1.16757812e+01, ... 1.10555556e+00, 4.44444444e-01]]) + >>> desc.save_descriptor('/path/to/outputfile.csv') # save the descriptor data (with feature names header) + """ + + # This is a strange way of doing it. However, the append=True option excludes length and charge, no idea why! + fn = [] + self.length() # sequence length + l = self.descriptor + fn.extend(self.featurenames) + self.calculate_MW(amide=amide) # molecular weight + mw = self.descriptor + fn.extend(self.featurenames) + self.calculate_charge(ph=ph, amide=amide) # net charge + c = self.descriptor + fn.extend(self.featurenames) + self.charge_density(ph=ph, amide=amide) # charge density + cd = self.descriptor + fn.extend(self.featurenames) + self.isoelectric_point(amide=amide) # pI + pi = self.descriptor + fn.extend(self.featurenames) + self.instability_index() # instability index + si = self.descriptor + fn.extend(self.featurenames) + self.aromaticity() # global aromaticity + ar = self.descriptor + fn.extend(self.featurenames) + self.aliphatic_index() # aliphatic index + ai = self.descriptor + fn.extend(self.featurenames) + self.boman_index() # Boman index + bi = self.descriptor + fn.extend(self.featurenames) + self.hydrophobic_ratio() # Hydrophobic ratio + hr = self.descriptor + fn.extend(self.featurenames) + + self.descriptor = np.concatenate((l, mw, c, cd, pi, si, ar, ai, bi, hr), axis=1) + self.featurenames = fn + + +class PeptideDescriptor(BaseDescriptor): + """Base class for peptide descriptors. The following **amino acid descriptor scales** are available for descriptor + calculation: + + - **AASI** (An amino acid selectivity index scale for helical antimicrobial peptides, *[1] D. Juretić, D. Vukicević, N. Ilić, N. Antcheva, A. Tossi, J. Chem. Inf. Model. 2009, 49, 2873–2882.*) + - **ABHPRK** (modlabs inhouse physicochemical feature scale (Acidic, Basic, Hydrophobic, Polar, aRomatic, Kink-inducer) + - **argos** (Argos hydrophobicity amino acid scale, *[2] Argos, P., Rao, J. K. M. & Hargrave, P. A., Eur. J. Biochem. 2005, 128, 565–575.*) + - **bulkiness** (Amino acid side chain bulkiness scale, *[3] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*) + - **charge_phys** (Amino acid charge at pH 7.0 - Hystidine charge +0.1.) + - **charge_acid** (Amino acid charge at acidic pH - Hystidine charge +1.0.) + - **cougar** (modlabs inhouse selection of global peptide descriptors) + - **eisenberg** (the Eisenberg hydrophobicity consensus amino acid scale, *[4] D. Eisenberg, R. M. Weiss, T. C. Terwilliger, W. Wilcox, Faraday Symp. Chem. Soc. 1982, 17, 109.*) + - **Ez** (potential that assesses energies of insertion of amino acid side chains into lipid bilayers, *[5] A. Senes, D. C. Chadi, P. B. Law, R. F. S. Walters, V. Nanda, W. F. DeGrado, J. Mol. Biol. 2007, 366, 436–448.*) + - **flexibility** (amino acid side chain flexibilitiy scale, *[6] R. Bhaskaran, P. K. Ponnuswamy, Int. J. Pept. Protein Res. 1988, 32, 241–255.*) + - **grantham** (amino acid side chain composition, polarity and molecular volume, *[8] Grantham, R. Science. 185, 862–864 (1974).*) + - **gravy** (GRAVY hydrophobicity amino acid scale, *[9] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*) + - **hopp-woods** (Hopp-Woods amino acid hydrophobicity scale,*[10] T. P. Hopp, K. R. Woods, Proc. Natl. Acad. Sci. 1981, 78, 3824–3828.*) + - **ISAECI** (Isotropic Surface Area (ISA) and Electronic Charge Index (ECI) of amino acid side chains, *[11] E. R. Collantes, W. J. Dunn, J. Med. Chem. 1995, 38, 2705–2713.*) + - **janin** (Janin hydrophobicity amino acid scale, *[12] J. L. Cornette, K. B. Cease, H. Margalit, J. L. Spouge, J. A. Berzofsky, C. DeLisi, J. Mol. Biol. 1987, 195, 659–685.*) + - **kytedoolittle** (Kyte & Doolittle hydrophobicity amino acid scale, *[13] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*) + - **levitt_alpha** (Levitt amino acid alpha-helix propensity scale, extracted from http://web.expasy.org/protscale. *[14] M. Levitt, Biochemistry 1978, 17, 4277-4285.*) + - **MSS** (A graph-theoretical index that reflects topological shape and size of amino acid side chains, *[15] C. Raychaudhury, A. Banerjee, P. Bag, S. Roy, J. Chem. Inf. Comput. Sci. 1999, 39, 248–254.*) + - **MSW** (Amino acid scale based on a PCA of the molecular surface based WHIM descriptor (MS-WHIM), extended to natural amino acids, *[16] A. Zaliani, E. Gancia, J. Chem. Inf. Comput. Sci 1999, 39, 525–533.*) + - **pepArc** (modlabs pharmacophoric feature scale, dimensions are: hydrophobicity, polarity, positive charge, negative charge, proline.) + - **pepcats** (modlabs pharmacophoric feature based PEPCATS scale, *[17] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*) + - **polarity** (Amino acid polarity scale, *[18] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*) + - **PPCALI** (modlabs inhouse scale derived from a PCA of 143 amino acid property scales, *[19] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*) + - **refractivity** (Relative amino acid refractivity values, *[20] T. L. McMeekin, M. Wilensky, M. L. Groves, Biochem. Biophys. Res. Commun. 1962, 7, 151–156.*) + - **t_scale** (A PCA derived scale based on amino acid side chain properties calculated with 6 different probes of the GRID program, *[21] M. Cocchi, E. Johansson, Quant. Struct. Act. Relationships 1993, 12, 1–8.*) + - **TM_tend** (Amino acid transmembrane propensity scale, extracted from http://web.expasy.org/protscale, *[22] Zhao, G., London E. Protein Sci. 2006, 15, 1987-2001.*) + - **z3** (The original three dimensional Z-scale, *[23] S. Hellberg, M. Sjöström, B. Skagerberg, S. Wold, J. Med. Chem. 1987, 30, 1126–1135.*) + - **z5** (The extended five dimensional Z-scale, *[24] M. Sandberg, L. Eriksson, J. Jonsson, M. Sjöström, S. Wold, J. Med. Chem. 1998, 41, 2481–2491.*) + + Further, amino acid scale independent methods can be calculated with help of the :class:`GlobalDescriptor` class. + + """ + + def __init__(self, seqs, scalename="Eisenberg"): + """ + :param seqs: a .fasta file with sequences, a list of sequences or a single sequence as string to calculate the + descriptor values for. + :param scalename: {str} name of the amino acid scale (one of the given list above) used to calculate the + descriptor values + :return: initialized attributes :py:attr:`sequences`, :py:attr:`names` and dictionary :py:attr:`scale` with + amino acid scale values of the scale name in :py:attr:`scalename`. + :Example: + + >>> AMP = PeptideDescriptor('KLLKLLKKLLKLLK','pepcats') + >>> AMP.sequences + ['KLLKLLKKLLKLLK'] + >>> seqs = PeptideDescriptor('/Path/to/file.fasta', 'eisenberg') # load sequences from .fasta file + >>> seqs.sequences + ['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'...] + """ + super(PeptideDescriptor, self).__init__(seqs) + self.scalename, self.scale = load_scale(scalename.lower()) + self.all_moms = list() # for passing hydrophobic moments to calculate_profile + self.all_globs = list() # for passing global to calculate_profile + + def load_scale(self, scalename): + """Method to load amino acid values from a given scale + + :param scalename: {str} name of the amino acid scale to be loaded. + :return: loaded amino acid scale values in a dictionary in the attribute :py:attr:`scale`. + + .. seealso:: :func:`modlamp.core.load_scale()` + """ + self.scalename, self.scale = load_scale(scalename.lower()) + + def calculate_autocorr(self, window, append=False): + """Method for auto-correlating the amino acid values for a given descriptor scale + + :param window: {int} correlation window for descriptor calculation in a sliding window approach + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`. + :Example: + + >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','PPCALI') + >>> AMP.calculate_autocorr(7) + >>> AMP.descriptor + array([[ 1.28442339e+00, 1.29025116e+00, 1.03240901e+00, .... ]]) + >>> AMP.descriptor.shape + (1, 133) + + .. versionchanged:: v.2.3.0 + """ + desc = Parallel(n_jobs=-1)( + delayed(_one_autocorr)(seq, window, self.scale) for seq in self.sequences + ) + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc) + + def calculate_crosscorr(self, window, append=False): + """Method for cross-correlating the amino acid values for a given descriptor scale + + :param window: {int} correlation window for descriptor calculation in a sliding window approach + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`. + :Example: + + >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','pepcats') + >>> AMP.calculate_crosscorr(7) + >>> AMP.descriptor + array([[ 0.6875 , 0.46666667, 0.42857143, 0.61538462, 0.58333333, ... ]]) + >>> AMP.descriptor.shape + (1, 147) + """ + desc = Parallel(n_jobs=-1)( + delayed(_one_crosscorr)(seq, window, self.scale) for seq in self.sequences + ) + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc) + + def calculate_moment(self, window=1000, angle=100, modality="max", append=False): + """Method for calculating the maximum or mean moment of the amino acid values for a given descriptor scale and + window. + + :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the + window, the length of the sequence is taken. So if the default window of 1000 is chosen, for all sequences + shorter than 1000, the **global** hydrophobic moment will be calculated. Otherwise, the maximal + hydrophiobic moment for the chosen window size found in the sequence will be returned. + :param angle: {int} angle in which to calculate the moment. **100** for alpha helices, **180** for beta sheets. + :param modality: {'all', 'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment. If all, + moments for all windows are returned. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global + values in :py:attr:`all_moms` (needed for the :py:func:`calculate_profile` method) + :Example: + + >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL', 'eisenberg') + >>> AMP.calculate_moment() + >>> AMP.descriptor + array([[ 0.48790226]]) + """ + if self.scale["A"] == list: + print( + "\n Descriptor moment calculation is only possible for one dimensional descriptors.\n" + ) + + else: + desc = [] + for seq in self.sequences: + wdw = min( + window, len(seq) + ) # if sequence is shorter than window, take the whole sequence instead + mtrx = [] + mwdw = [] + + for aa in range(len(seq)): + mtrx.append(self.scale[str(seq[aa])]) + + for i in range(len(mtrx) - wdw + 1): + mwdw.append(sum(mtrx[i : i + wdw], [])) + + mwdw = np.asarray(mwdw) + rads = ( + angle * (np.pi / 180) * np.asarray(range(wdw)) + ) # calculate actual moment (radial) + vcos = (mwdw * np.cos(rads)).sum(axis=1) + vsin = (mwdw * np.sin(rads)).sum(axis=1) + moms = np.sqrt(vsin**2 + vcos**2) / wdw + + if modality == "max": # take window with maximal value + moment = np.max(moms) + elif modality == "mean": # take average value over all windows + moment = np.mean(moms) + elif modality == "all": + moment = moms + else: + print( + '\nERROR!\nModality parameter is wrong, please choose between "all", "max" and "mean".\n' + ) + return + desc.append(moment) + self.all_moms.append(moms) + + desc = np.asarray(desc).reshape(len(desc), 1) # final descriptor array + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc) + + def calculate_global(self, window=1000, modality="max", append=False): + """Method for calculating a global / window averaging descriptor value of a given AA scale + + :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the + window, the length of the sequence is taken. + :param modality: {'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment. + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global + values in :py:attr:`all_globs` (needed for the :py:func:`calculate_profile` method) + :Example: + + >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','eisenberg') + >>> AMP.calculate_global(window=1000, modality='max') + >>> AMP.descriptor + array([[ 0.44875]]) + """ + desc = list() + for n, seq in enumerate(self.sequences): + wdw = min( + window, len(seq) + ) # if sequence is shorter than window, take the whole sequence instead + mtrx = [] + mwdw = [] + + for l in range(len(seq)): # translate AA sequence into values + mtrx.append(self.scale[str(seq[l])]) + + for i in range(len(mtrx) - wdw + 1): + mwdw.append( + sum(mtrx[i : i + wdw], []) + ) # list of all the values for the different windows + + mwdw = np.asarray(mwdw) + glob = np.sum(mwdw, axis=1) / float(wdw) + outglob = float() + + if modality in ["max", "mean"]: + if modality == "max": + outglob = np.max( + glob + ) # returned moment will be the maximum of all windows + elif modality == "mean": + outglob = np.mean( + glob + ) # returned moment will be the mean of all windows + else: + print( + 'Modality parameter is wrong, please choose between "max" and "mean"\n.' + ) + return + desc.append(outglob) + self.all_globs.append(glob) + + desc = np.asarray(desc).reshape(len(desc), 1) + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc) + + def calculate_profile(self, prof_type="uH", window=7, append=False): + """Method for calculating hydrophobicity or hydrophobic moment profiles for given sequences and fitting for + slope and intercept. The hydrophobicity scale used is "eisenberg" + + :param prof_type: prof_type of profile, available: 'H' for hydrophobicity or 'uH' for hydrophobic moment + :param window: {int} size of sliding window used (odd-numbered). + :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the + attribute :py:attr:`descriptor`. + :return: Fitted slope and intercept of calculated profile for every given sequence in the attribute + :py:attr:`descriptor`. + :Example: + + >>> AMP = PeptideDescriptor('KLLKLLKKVVGALG','kytedoolittle') + >>> AMP.calculate_profile(prof_type='H') + >>> AMP.descriptor + array([[ 0.03731293, 0.19246599]]) + """ + if prof_type == "uH": + self.calculate_moment(window=window) + y_vals = self.all_moms + elif prof_type == "H": + self.calculate_global(window=window) + y_vals = self.all_globs + else: + print( + 'prof_type parameter is unknown, choose "uH" for hydrophobic moment or "H" for hydrophobicity\n.' + ) + sys.exit() + + desc = list() + for n, seq in enumerate(self.sequences): + x_vals = range(len(seq))[int((window - 1) / 2) : -int((window - 1) / 2)] + if len(seq) <= window: + slope, intercept, r_value, p_value, std_err = [0, 0, 0, 0, 0] + else: + slope, intercept, r_value, p_value, std_err = stats.linregress( + x_vals, y_vals[n] + ) + desc.append([slope, intercept]) + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc) + + def calculate_arc(self, modality="max", append=False): + """Method for calculating property arcs as seen in the helical wheel plot. Use for binary amino acid scales only. + + :param modality: modality of the arc to calculate, to choose between "max" and "mean". + :param append: if true, append to current descriptor stored in the descriptor attribute. + :return: calculated descriptor as numpy.array in the descriptor attribute. + + :Example: + + >>> arc = PeptideDescriptor("KLLKLLKKLLKLLK", scalename="peparc") + >>> arc.calculate_arc(modality="max", append=False) + >>> arc.descriptor + array([[200, 160, 160, 0, 0]]) + """ + desc = Parallel(n_jobs=-1)( + delayed(_one_arc)(seq, modality, self.scale) for seq in self.sequences + ) + + # Converts each of the amino acids to descriptor vector + for seq in self.sequences: + + # desc_mat = [] + # for aa in seq: + # desc_mat.append(self.scale[aa]) + # desc_mat = np.asarray(desc_mat) + # + # # Check descriptor dimension + # desc_dim = desc_mat.shape[1] + # + # # list to store descriptor values for all windows + # allwindows_arc = [] + # + # if len(seq) > 18: + # window = 18 + # # calculates number of windows in sequence + # num_windows = len(seq) - window + # else: + # window = len(seq) + # num_windows = 1 + # + # # loop through all windows + # for j in range(num_windows): + # # slices descriptor matrix into current window + # window_mat = desc_mat[j:j + window, :] + # + # # defines order of amino acids in helical projection + # order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7] + # + # # orders window descriptor matrix into helical projection order + # ordered = [] + # for pos in order: + # try: + # ordered.append(window_mat[pos, :]) + # except: + # # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions + # ordered.append([2] * desc_dim) + # ordered = np.asarray(ordered) + # + # window_arc = [] + # + # # loop through pharmacophoric features + # for m in range(desc_dim): + # all_arcs = [] # stores all arcs that can be found of a pharmacophoric feature + # arc = 0 + # + # for n in range(18): # for all positions in helix, regardless of sequence length + # if ordered[n, m] == 0: # if position does not contain pharmacophoric feature + # all_arcs.append(arc) # append previous arc to all arcs list + # arc = 0 # arc is initialized + # elif ordered[n, m] == 1: # if position contains pharmacophoric feature(PF), elongate arc by 20° + # arc += 20 + # elif ordered[n, m] == 2: # if position doesn't contain amino acid: + # if ordered[n - 1, m] == 1: # if previous position contained PF add 10° + # arc += 10 + # elif ordered[n - 1, m] == 0: # if previous position didn't contain PF don't add anything + # arc += 0 + # elif ordered[ + # n - 2, m] == 1: # if previous position is empty then check second previous for PF + # arc += 10 + # if n == 17: # if we are at the last position check for position n=0 instead of next position. + # if ordered[0, m] == 1: # if it contains PF add 10° extra + # arc += 10 + # else: # if next position contains PF add 10° extra + # if ordered[n + 1, m] == 1: + # arc += 10 + # elif ordered[n + 1, m] == 0: + # arc += 0 + # else: # if next position is empty check for 2nd next position + # if n == 16: + # if ordered[0, m] == 1: + # arc += 10 + # else: + # if ordered[n + 2, m] == 1: + # arc += 10 + # + # all_arcs.append(arc) + # if not arc == 360: + # arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together + # all_arcs = [arc0] + all_arcs[1:] + # + # window_arc.append(np.max(all_arcs)) # append to window arcs the maximum arc of this PF + # allwindows_arc.append(window_arc) # append all PF arcs of this window + # + # allwindows_arc = np.asarray(allwindows_arc) + # + # if modality == 'max': + # final_arc = np.max(allwindows_arc, axis=0) # calculate maximum / mean arc along all windows + # elif modality == 'mean': + # final_arc = np.mean(allwindows_arc, axis=0) + # else: + # print('modality is unknown, please choose between "max" and "mean"\n.') + # sys.exit() + + if append: + self.descriptor = np.hstack((self.descriptor, np.array(desc))) + else: + self.descriptor = np.array(desc)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotWheels/helical_wheel.py Mon Jun 05 02:44:43 2023 +0000 @@ -0,0 +1,562 @@ +import matplotlib + +matplotlib.use("Agg") + +import matplotlib.lines as lines +import matplotlib.patches as patches +import matplotlib.pyplot as plt + +# from mpl_toolkits.mplot3d import Axes3D +import numpy as np +from scipy.stats.kde import gaussian_kde + +from plotWheels.core import load_scale +from plotWheels.descriptors import PeptideDescriptor + + +def helical_wheel( + sequence, + colorcoding="rainbow", + text_color=None, + lineweights=True, + filename=None, + seq=False, + moment=False, + seqRange=1, + t_size=32, + rot=float(90), + dpi=150, + numbering=True, +): + """A function to project a given peptide sequence onto a helical wheel plot. It can be useful to illustrate the + properties of alpha-helices, like positioning of charged and hydrophobic residues along the sequence. + + :param sequence: {str} the peptide sequence for which the helical wheel should be drawn + :param colorcoding: {str} the color coding to be used, available: *rainbow*, *charge*, *polar*, *simple*, + *amphipathic*, *custom_input*, *none* + :param lineweights: {boolean} defines whether connection lines decrease in thickness along the sequence + :param filename: {str} filename where to save the plot. *default = None* --> show the plot + :param seq: {bool} whether the amino acid sequence should be plotted as a title + :param moment: {bool} whether the Eisenberg hydrophobic moment should be calculated and plotted + :param seqRange: {int} starting value of residue location in sequence + :param t_size: {int} text size + :param rot: {float} rotation by radians --> converted to degrees. + :param dpi: {int} dpi parameter for saved files + :return: a helical wheel projection plot of the given sequence (interactively or in **filename**) + :Example: + + >>> helical_wheel('GLFDIVKKVVGALG') + >>> helical_wheel('KLLKLLKKLLKLLK', colorcoding='charge') + >>> helical_wheel('AKLWLKAGRGFGRG', colorcoding='none', lineweights=False) + >>> helical_wheel('ACDEFGHIKLMNPQRSTVWY') + + .. image:: ../docs/static/wheel1.png + :height: 300px + .. image:: ../docs/static/wheel2.png + :height: 300px + .. image:: ../docs/static/wheel3.png + :height: 300px + .. image:: ../docs/static/wheel4.png + :height: 300px + + .. versionadded:: v2.1.5 + """ + # color mappings + aa = [ + "A", + "C", + "D", + "E", + "F", + "G", + "H", + "I", + "K", + "L", + "M", + "N", + "P", + "Q", + "R", + "S", + "T", + "V", + "W", + "Y", + ] + if colorcoding == type(str): + f_rainbow = [ + "#3e3e28", + "#ffcc33", + "#b30047", + "#b30047", + "#ffcc33", + "#3e3e28", + "#80d4ff", + "#ffcc33", + "#0047b3", + "#ffcc33", + "#ffcc33", + "#b366ff", + "#29a329", + "#b366ff", + "#0047b3", + "#ff66cc", + "#ff66cc", + "#ffcc33", + "#ffcc33", + "#ffcc33", + ] + f_charge = [ + "#000000", + "#000000", + "#ff4d94", + "#ff4d94", + "#000000", + "#000000", + "#80d4ff", + "#000000", + "#80d4ff", + "#000000", + "#000000", + "#000000", + "#000000", + "#000000", + "#80d4ff", + "#000000", + "#000000", + "#000000", + "#000000", + "#000000", + ] + f_polar = [ + "#000000", + "#000000", + "#80d4ff", + "#80d4ff", + "#000000", + "#000000", + "#80d4ff", + "#000000", + "#80d4ff", + "#000000", + "#000000", + "#80d4ff", + "#000000", + "#80d4ff", + "#80d4ff", + "#80d4ff", + "#80d4ff", + "#000000", + "#000000", + "#000000", + ] + f_simple = [ + "#ffcc33", + "#ffcc33", + "#0047b3", + "#0047b3", + "#ffcc33", + "#7f7f7f", + "#0047b3", + "#ffcc33", + "#0047b3", + "#ffcc33", + "#ffcc33", + "#0047b3", + "#ffcc33", + "#0047b3", + "#0047b3", + "#0047b3", + "#0047b3", + "#ffcc33", + "#ffcc33", + "#ffcc33", + ] + f_none = ["#ffffff"] * 20 + f_amphi = [ + "#ffcc33", + "#29a329", + "#b30047", + "#b30047", + "#f79318", + "#80d4ff", + "#0047b3", + "#ffcc33", + "#0047b3", + "#ffcc33", + "#ffcc33", + "#80d4ff", + "#29a329", + "#80d4ff", + "#0047b3", + "#80d4ff", + "#80d4ff", + "#ffcc33", + "#f79318", + "#f79318", + ] + t_rainbow = [ + "w", + "k", + "w", + "w", + "k", + "w", + "k", + "k", + "w", + "k", + "k", + "k", + "k", + "k", + "w", + "k", + "k", + "k", + "k", + "k", + ] + t_charge = [ + "w", + "w", + "k", + "k", + "w", + "w", + "k", + "w", + "k", + "w", + "w", + "w", + "w", + "w", + "k", + "w", + "w", + "w", + "w", + "w", + ] + t_polar = [ + "w", + "w", + "k", + "k", + "w", + "w", + "k", + "w", + "k", + "w", + "w", + "k", + "w", + "k", + "k", + "k", + "k", + "w", + "w", + "w", + ] + t_simple = [ + "k", + "k", + "w", + "w", + "k", + "w", + "w", + "k", + "w", + "k", + "k", + "k", + "k", + "w", + "w", + "w", + "w", + "k", + "k", + "k", + ] + t_none = ["k"] * 20 + t_amphi = [ + "k", + "k", + "w", + "w", + "w", + "k", + "w", + "k", + "w", + "k", + "k", + "k", + "w", + "k", + "w", + "k", + "k", + "k", + "w", + "w", + ] + d_eisberg = load_scale("eisenberg")[1] # eisenberg hydrophobicity values for HM + else: + f_custom = colorcoding + t_custom = text_color + d_eisberg = load_scale("eisenberg")[1] + + if lineweights: + lw = np.arange(0.1, 5.5, 5.0 / (len(sequence) - 1)) # line thickness array + lw = lw[::-1] # inverse order + else: + lw = [2.0] * (len(sequence) - 1) + # check which color coding to use + if colorcoding == type(str): + if colorcoding == "rainbow": + df = dict(zip(aa, f_rainbow)) + dt = dict(zip(aa, t_rainbow)) + elif colorcoding == "charge": + df = dict(zip(aa, f_charge)) + dt = dict(zip(aa, t_charge)) + elif colorcoding == "polar": + df = dict(zip(aa, f_polar)) + dt = dict(zip(aa, t_polar)) + elif colorcoding == "simple": + df = dict(zip(aa, f_simple)) + dt = dict(zip(aa, t_simple)) + elif colorcoding == "none": + df = dict(zip(aa, f_none)) + dt = dict(zip(aa, t_none)) + elif colorcoding == "amphipathic": + df = dict(zip(aa, f_amphi)) + dt = dict(zip(aa, t_amphi)) + else: + print("Unknown color coding, 'rainbow' used instead") + df = dict(zip(aa, f_rainbow)) + dt = dict(zip(aa, t_rainbow)) + else: + df = dict(zip(aa, f_custom)) + dt = dict(zip(aa, t_custom)) + + # degree to radian + deg = np.arange(float(len(sequence))) * -100.0 + deg = [d + rot for d in deg] # start at 270 degree in unit circle (on top) + rad = np.radians(deg) + + # dict for coordinates and eisenberg values + d_hydro = dict(zip(rad, [0.0] * len(rad))) + + # create figure + fig = plt.figure(frameon=False, figsize=(10, 10)) + ax = fig.add_subplot(111) + old = None + hm = list() + + # iterate over sequence + for i, r in enumerate(rad): + new = (np.cos(r), np.sin(r)) # new AA coordinates + if i < 18: + # plot the connecting lines + if old is not None: + line = lines.Line2D( + (old[0], new[0]), + (old[1], new[1]), + transform=ax.transData, + color="k", + linewidth=lw[i - 1], + ) + line.set_zorder(1) # 1 = level behind circles + ax.add_line(line) + elif 17 < i < 36: + line = lines.Line2D( + (old[0], new[0]), + (old[1], new[1]), + transform=ax.transData, + color="k", + linewidth=lw[i - 1], + ) + line.set_zorder(1) # 1 = level behind circles + ax.add_line(line) + new = (np.cos(r) * 1.2, np.sin(r) * 1.2) + elif i == 36: + line = lines.Line2D( + (old[0], new[0]), + (old[1], new[1]), + transform=ax.transData, + color="k", + linewidth=lw[i - 1], + ) + line.set_zorder(1) # 1 = level behind circles + ax.add_line(line) + new = (np.cos(r) * 1.4, np.sin(r) * 1.4) + else: + new = (np.cos(r) * 1.4, np.sin(r) * 1.4) + + # plot circles + circ = patches.Circle( + new, + radius=0.125, + transform=ax.transData, + edgecolor="k", + facecolor=df[sequence[i]], + ) + circ.set_zorder(2) # level in front of lines + ax.add_patch(circ) + + # check if N- or C-terminus and add subscript, then plot AA letter + if numbering: + size = t_size + if i == 0: + ax.text( + new[0], + new[1], + sequence[i] + "$_N$", + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + elif i == len(sequence) - 1: + ax.text( + new[0], + new[1], + sequence[i] + "$_C$", + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + else: + seqRange += 1 + ax.text( + new[0], + new[1], + sequence[i] + "$_{" + str(seqRange) + "}$", + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + + eb = d_eisberg[sequence[i]][0] # eisenberg value for this AA + hm.append( + [eb * new[0], eb * new[1]] + ) # save eisenberg hydrophobicity vector value to later calculate HM + + old = (np.cos(r), np.sin(r)) # save as previous coordinates + + else: + size = t_size + if i == 0: + ax.text( + new[0], + new[1], + sequence[i] + "$_N$", + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + elif i == len(sequence) - 1: + ax.text( + new[0], + new[1], + sequence[i] + "$_C$", + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + else: + ax.text( + new[0], + new[1], + sequence[i], + va="center", + ha="center", + transform=ax.transData, + size=size, + color=dt[sequence[i]], + fontweight="bold", + ) + + eb = d_eisberg[sequence[i]][0] # eisenberg value for this AA + hm.append( + [eb * new[0], eb * new[1]] + ) # save eisenberg hydrophobicity vector value to later calculate HM + + old = (np.cos(r), np.sin(r)) # save as previous coordinates + + # draw hydrophobic moment arrow if moment option + if moment: + v_hm = np.sum(np.array(hm), 0) + x = 0.0333 * v_hm[0] + y = 0.0333 * v_hm[1] + ax.arrow( + 0.0, + 0.0, + x, + y, + head_width=0.04, + head_length=0.03, + transform=ax.transData, + color="k", + linewidth=6.0, + ) + desc = PeptideDescriptor(sequence) # calculate hydrophobic moment + desc.calculate_moment() + if ( + abs(x) < 0.2 and y > 0.0 + ): # right positioning of HM text so arrow does not cover it + z = -0.2 + else: + z = 0.2 + plt.text( + 0.0, + z, + str(round(desc.descriptor[0][0], 3)), + fontdict={"fontsize": 20, "fontweight": "bold", "ha": "center"}, + ) + + # plot shape + if len(sequence) < 19: + ax.set_xlim(-1.2, 1.2) + ax.set_ylim(-1.2, 1.2) + else: + ax.set_xlim(-1.4, 1.4) + ax.set_ylim(-1.4, 1.4) + ax.spines["right"].set_visible(False) + ax.spines["top"].set_visible(False) + ax.spines["left"].set_visible(False) + ax.spines["bottom"].set_visible(False) + cur_axes = plt.gca() + cur_axes.axes.get_xaxis().set_visible(False) + cur_axes.axes.get_yaxis().set_visible(False) + plt.tight_layout() + + if seq: + plt.title(sequence, fontweight="bold", fontsize=20) + + # show or save plot + if filename: + plt.savefig(filename, dpi=dpi) + else: + plt.show()