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PYCOEVOL

A Python workflow to study protein-protein coevolution and interaction

Pycoevol is an integrated system for studying inter-protein coevolution and interaction. It automates the identification of contact points between protein partners, extending the general coevolution workflow consisting of: homologous sequence search; multiple sequence alignment computation; and coevolution analysis; with an improved selection of organisms and contact prediction.

It generates friendly output results: matrix of scores; histograms; heat-maps; PyMOL scripts and interaction maps. Additional information for common web-services can be retrieved from SIFTS.

Disclaimer

This software is provided "as is", with no explicit or implied warranties. Use this software at your own risk.

Copyright

This software is public domain, and everyone has the right to copy, distribute, reuse, modify, improve and debug it.

If you want to cite this piece of software/workflow use the following:

Fábio Madeira and Ludwig Krippahl. 2012. PYCOEVOL: A Python workflow to study protein-protein coevolution. Proceedings of the International conference on Bioinformatics Models, Methods and Algorithms - BIOINFORMATICS 2012, pp.143-9.

This work was partially supported by Portuguese National funds through Fundação para a Ciência e Tecnologia (FCT) under project CREMA PTDC/EIA-CCO/115999/2009.

Dependencies

Python 2.7.2, Biopython 1.58, Numpy 1.6.1, Matplotlib 1.1.0 and ClustalW

Optional: NCBI Blast+, NCBI's "refseq_protein" database, MUSCLE, MAFFT and SIFTS lst files

Usage

python Pycoevol.py input1 input2 [options]

-h, --help		show this help message and exit
 
-b PSIBLAST, --psiblast=PSIBLAST

				internet, local or custom
 
-a ALIGNMENT, --alignment=ALIGNMENT

				clustalw, muscle, mafft or custom
 
-c COEVOLUTION, --coevolution=COEVOLUTION

				mi, mie, rcwmi, cpvn, clm, vol, omes, pearson,spearman, mcbasc, quartets, sca or elsc
 
-i IDS, --id=IDS

-x CHAINS, --chain=CHAINS

-p PARAMETERFILE, --parameters=PARAMETERFILE

For a detailed overview on how to install and use Pycoevol, please refer to the User Guide.

Coevolution measures:

  • Mutual Information (mi) [Gloor et al, 2005]
  • MI by pair Entropy (mie) [Martin et al, 2005]
  • Row and Column Weighed MI (rcwmi) [Gouveia-Oliveira et al, 2007]
  • Contact Preferences, Volume Normalized (cpvn) [Glaser et al, 2001]
  • Contact PDB-derived Likelihood Matrix (clm) [Singer et al, 2002]
  • Residue-residue Volume Normalized (vol) [based on Esque et al, 2010]
  • Observed Minus Expected Squared (omes) [Kass and Horovitz, 2002]
  • Pearson’s correlation (pearson) [Göbel et al, 1994]
  • Spearman’s rank correlation (spearman) [Pazos et al, 1997]
  • McLachlan Based Substitution Correlation (mcbasc) [Fodor and Aldrich, 2004]
  • Quartets (quartets) [Galitsky, 2002]
  • Statistical Coupling Analysis (sca) [Lockless and Ranganathan, 1999]
  • Explicit Likelihood of Subset Covariation (elsc) [Dekker et al, 2004]

Pairwise distance measures:

  • ClustalW distance[Chenna et al, 2003]
  • p-distance [Jukes and Cantor, 1969]
  • Jukes-Cantor [Jukes and Cantor, 1969]
  • Kimura distance [Kimura, 1983]
  • Pairwise score using Dayhoff or Henikoff matrices [Dayhoff et al, 1978; Henikoff and Henikoff, 1992]

Fábio Madeira and Ludwig Krippahl, 2012

This work was partially supported by Portuguese National funds through Fundação para a Ciência e Tecnologia (FCT) under project CREMA PTDC/EIA-CCO/115999/2009.