Co-evolution techniques are reshaping the way we do structural bioinformatics

Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.

[1]  Kevin Karplus,et al.  Contact prediction using mutual information and neural nets , 2007, Proteins.

[2]  M B Swindells,et al.  A procedure for detecting structural domains in proteins , 1995, Protein science : a publication of the Protein Society.

[3]  Jiye Shi,et al.  Comparing co-evolution methods and their application to template-free protein structure prediction , 2016, Bioinform..

[4]  Yang Zhang,et al.  High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling , 2013, Bioinform..

[5]  C. Sander,et al.  Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.

[6]  Terence Hwa,et al.  Coevolutionary signals across protein lineages help capture multiple protein conformations , 2013, Proceedings of the National Academy of Sciences.

[7]  Graziano Pesole,et al.  Correlated substitution analysis and the prediction of amino acid structural contacts , 2007, Briefings Bioinform..

[8]  E. Aurell,et al.  Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  G. Stormo,et al.  Correlated mutations in models of protein sequences: phylogenetic and structural effects , 1999 .

[10]  David T. Jones,et al.  MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..

[11]  Modesto Orozco,et al.  Residues Coevolution Guides the Systematic Identification of Alternative Functional Conformations in Proteins. , 2016, Structure.

[12]  Marcin J. Skwark,et al.  PconsC: combination of direct information methods and alignments improves contact prediction , 2013, Bioinform..

[13]  Zhen Li,et al.  Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2016, bioRxiv.

[14]  Ricardo N Dos Santos,et al.  Dimeric interactions and complex formation using direct coevolutionary couplings , 2015, Scientific Reports.

[15]  O. Brock,et al.  Combining Physicochemical and Evolutionary Information for Protein Contact Prediction , 2014, PloS one.

[16]  P Fariselli,et al.  Prediction of contact maps with neural networks and correlated mutations. , 2001, Protein engineering.

[17]  Qishi Du,et al.  Improving the thermostability of alpha-amylase by combinatorial coevolving-site saturation mutagenesis , 2012, BMC Bioinformatics.

[18]  Markus Gruber,et al.  CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations , 2014, Bioinform..

[19]  Lucy J. Colwell,et al.  Inferring interaction partners from protein sequences , 2016, Proceedings of the National Academy of Sciences.

[20]  Khalid Kunji,et al.  Coevolution Analysis of HIV-1 Envelope Glycoprotein Complex , 2015, PloS one.

[21]  Daniel J Rigden,et al.  Applications of contact predictions to structural biology , 2017, IUCrJ.

[22]  Simone Marsili,et al.  Large-Scale Conformational Transitions and Dimerization Are Encoded in the Amino-Acid Sequences of Hsp70 Chaperones , 2015, PLoS Comput. Biol..

[23]  Thomas A. Hopf,et al.  Sequence co-evolution gives 3D contacts and structures of protein complexes , 2014, eLife.

[24]  D. Baker,et al.  Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.

[25]  Thomas A. Hopf,et al.  Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.

[26]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction: Progress and new directions in round XI , 2016, Proteins.

[27]  Massimiliano Pontil,et al.  PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..

[28]  Jinbo Xu,et al.  Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2016 .

[29]  Thomas A. Hopf,et al.  Structured States of Disordered Proteins from Genomic Sequences , 2016, Cell.

[30]  David E. Kim,et al.  Large-scale determination of previously unsolved protein structures using evolutionary information , 2015, eLife.

[31]  Carlo Baldassi,et al.  Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis , 2016, Proceedings of the National Academy of Sciences.

[32]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[33]  D. Baker,et al.  Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information , 2014, eLife.

[34]  Marcin J. Skwark,et al.  Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns , 2014, PLoS Comput. Biol..

[35]  G J Barton,et al.  Continuous and discontinuous domains: An algorithm for the automatic generation of reliable protein domain definitions , 1995, Protein science : a publication of the Protein Society.

[36]  Andrea Pagnani,et al.  Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon , 2015, PloS one.

[37]  P Fariselli,et al.  Progress in predicting inter‐residue contacts of proteins with neural networks and correlated mutations , 2001, Proteins.

[38]  Burkhard Rost,et al.  FreeContact: fast and free software for protein contact prediction from residue co-evolution , 2014, BMC Bioinformatics.

[39]  John P. Barton,et al.  The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing , 2014, PLoS Comput. Biol..

[40]  Jessica Andreani,et al.  Lessons from (co‐)evolution in the docking of proteins and peptides for CAPRI Rounds 28–35 , 2017, Proteins.

[41]  Johannes Söding,et al.  Bbcontacts: Prediction of Β-strand Pairing from Direct Coupling Patterns , 2015, Bioinform..

[42]  C. Sander,et al.  Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations? , 1994, Protein engineering.

[43]  O. Mayans,et al.  Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds , 2016, IUCrJ.

[44]  C. Sander,et al.  Correlated mutations and residue contacts in proteins , 1994, Proteins.

[45]  Dong Xu,et al.  OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method , 2017, J. Comput. Biol..

[46]  A. Valencia,et al.  Improving contact predictions by the combination of correlated mutations and other sources of sequence information. , 1997, Folding & design.

[47]  Timothy Nugent,et al.  Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis , 2012, Proceedings of the National Academy of Sciences.

[48]  Daniel J Rigden,et al.  Use of covariance analysis for the prediction of structural domain boundaries from multiple protein sequence alignments. , 2002, Protein engineering.

[49]  I. Bahar,et al.  Sequence Evolution Correlates with Structural Dynamics , 2012, Molecular biology and evolution.

[50]  D. Haussler,et al.  Information‐theoretic dissection of pairwise contact potentials , 2002, Proteins.

[51]  Michael I Sadowski Prediction of protein domain boundaries from inverse covariances , 2013, Proteins.

[52]  Georgios A. Pavlopoulos,et al.  Protein structure determination using metagenome sequence data , 2017, Science.

[53]  Kelly M. Thayer,et al.  Evolutionary Covariance Combined with Molecular Dynamics Predicts a Framework for Allostery in the MutS DNA Mismatch Repair Protein , 2017, The journal of physical chemistry. B.

[54]  David E. Kim,et al.  Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta , 2016, Proteins.

[55]  Alfonso Valencia,et al.  A graphical interface for correlated mutations and other protein structure prediction methods , 1997, Comput. Appl. Biosci..

[56]  Jorick Franceus,et al.  Correlated positions in protein evolution and engineering , 2017, Journal of Industrial Microbiology & Biotechnology.

[57]  Guilhem Faure,et al.  InterEvScore: a novel coarse-grained interface scoring function using a multi-body statistical potential coupled to evolution , 2013, Bioinform..

[58]  Thomas A. Hopf,et al.  Protein structure prediction from sequence variation , 2012, Nature Biotechnology.

[59]  Thomas A. Hopf,et al.  Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing , 2012, Cell.

[60]  A. Valencia,et al.  From residue coevolution to protein conformational ensembles and functional dynamics , 2015, Proceedings of the National Academy of Sciences.

[61]  M. Weigt,et al.  Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 , 2015, bioRxiv.

[62]  Faruck Morcos,et al.  From structure to function: the convergence of structure based models and co-evolutionary information. , 2014, Physical chemistry chemical physics : PCCP.

[63]  Sivaraman Balakrishnan,et al.  Learning generative models for protein fold families , 2011, Proteins.

[64]  Zhendong Bei,et al.  COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming , 2016, Proteins.

[65]  Saurav Mallik,et al.  Coevolutionary constraints in the sequence‐space of macromolecular complexes reflect their self‐assembly pathways , 2017, Proteins.

[66]  Thomas A. Hopf,et al.  Mutation effects predicted from sequence co-variation , 2017, Nature Biotechnology.