Applications of contact predictions to structural biology

Recent developments allow the extraction of accurate contact predictions from multiple protein-sequence alignments. This review illustrates the manifold ways in which this information may assist the experimental structural biologist.

[1]  Johannes Söding,et al.  The HHpred interactive server for protein homology detection and structure prediction , 2005, Nucleic Acids Res..

[2]  Martin Zacharias,et al.  SAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes. , 2016, Structure.

[3]  Serge X. Cohen,et al.  Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7 , 2008, Nature Protocols.

[4]  Nathaniel Echols,et al.  Improved low-resolution crystallographic refinement with Phenix and Rosetta , 2013, Nature Methods.

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

[6]  Kevin Cowtan,et al.  The Buccaneer software for automated model building. 1. Tracing protein chains. , 2006, Acta crystallographica. Section D, Biological crystallography.

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

[8]  Frank Alber,et al.  γ-TEMPy: Simultaneous Fitting of Components in 3D-EM Maps of Their Assembly Using a Genetic Algorithm , 2015, Structure.

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

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

[11]  Cristina Marino Buslje,et al.  I-COMS: Interprotein-COrrelated Mutations Server , 2015, Nucleic Acids Res..

[12]  Cathy H. Wu,et al.  Prediction of contact matrix for protein-protein interaction , 2013, Bioinform..

[13]  Wei Zheng,et al.  A large-scale comparative assessment of methods for residue–residue contact prediction , 2016, Briefings Bioinform..

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

[15]  Leszek Rychlewski,et al.  XtalPred: a web server for prediction of protein crystallizability , 2007, Bioinform..

[16]  Debora S. Marks,et al.  Protein structure determination by combining sparse NMR data with evolutionary couplings , 2015, Nature Methods.

[17]  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.

[18]  Pierre Tufféry,et al.  InterEvDock: a docking server to predict the structure of protein–protein interactions using evolutionary information , 2016, Nucleic Acids Res..

[19]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.

[20]  P. Sliż,et al.  Protein structure determination by exhaustive search of Protein Data Bank derived databases , 2010, Proceedings of the National Academy of Sciences.

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

[22]  Carlos Oscar S Sorzano,et al.  3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling , 2016, Biophysical journal.

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

[24]  David Baker,et al.  De novo protein structure determination from near-atomic resolution cryo-EM maps , 2015, Nature Methods.

[25]  Kuldip K. Paliwal,et al.  Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins , 2016, Bioinform..

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

[27]  Debora S Marks,et al.  Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity. , 2015, Structure.

[28]  Wayne A. Hendrickson,et al.  Anomalous diffraction in crystallographic phase evaluation , 2014, Quarterly Reviews of Biophysics.

[29]  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.

[30]  Zbigniew Dauter,et al.  New approaches to high-throughput phasing. , 2002, Current opinion in structural biology.

[31]  David Baker,et al.  Modeling Symmetric Macromolecular Structures in Rosetta3 , 2011, PloS one.

[32]  L. S. Swapna,et al.  Use of evolutionary information in the fitting of atomic level protein models in low resolution cryo-EM map of a protein assembly improves the accuracy of the fitting. , 2016, Journal of structural biology.

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

[34]  Jianlin Cheng,et al.  CONFOLD: Residue‐residue contact‐guided ab initio protein folding , 2015, Proteins.

[35]  Alexandre M J J Bonvin,et al.  Performance of the WeNMR CS-Rosetta3 web server in CASD-NMR , 2015, Journal of Biomolecular NMR.

[36]  Jeffrey J. Gray,et al.  Computational modeling of membrane proteins , 2015, Proteins.

[37]  B. Rupp Biomolecular Crystallography: Principles, Practice, and Application to Structural Biology , 2009 .

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

[39]  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.

[40]  Eugene Krissinel,et al.  Stock-based detection of protein oligomeric states in jsPISA , 2015, Nucleic Acids Res..

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

[42]  A. Biegert,et al.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2011, Nature Methods.

[43]  S. Griffis EDITOR , 1997, Journal of Navigation.

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

[45]  Ronan M Keegan,et al.  AMPLE: a cluster-and-truncate approach to solve the crystal structures of small proteins using rapidly computed ab initio models. , 2012, Acta crystallographica. Section D, Biological crystallography.

[46]  Jie Hou,et al.  ConEVA: a toolbox for comprehensive assessment of protein contacts , 2016, BMC Bioinformatics.

[47]  J. Koenderink Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[48]  Liskin Swint-Kruse,et al.  Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores , 2015, Proteins.

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

[50]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Alan Brown,et al.  Structure of the large ribosomal subunit from human mitochondria , 2014, Science.

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

[53]  Daniel J. Rigden,et al.  ConKit: a python interface to contact predictions , 2017, Bioinform..

[54]  John L. Rubinstein,et al.  Models for the a subunits of the Thermus thermophilus V/A-ATPase and Saccharomyces cerevisiae V-ATPase enzymes by cryo-EM and evolutionary covariance , 2016, Proceedings of the National Academy of Sciences.

[55]  A. Valencia,et al.  Emerging methods in protein co-evolution , 2013, Nature Reviews Genetics.

[56]  Daniel J. Rigden,et al.  Potential DNA binding and nuclease functions of ComEC domains characterized in silico , 2016, Proteins.

[57]  Zhiyong Wang,et al.  Predicting protein contact map using evolutionary and physical constraints by integer programming , 2013, Bioinform..

[58]  Randy J. Read,et al.  Improved molecular replacement by density- and energy-guided protein structure optimization , 2011, Nature.

[59]  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.

[60]  Michael Sattler,et al.  NMR approaches for structural analysis of multidomain proteins and complexes in solution. , 2014, Progress in nuclear magnetic resonance spectroscopy.

[61]  Dong Xu,et al.  FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking , 2014, Bioinform..

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

[63]  Dmitri I. Svergun,et al.  pyDockSAXS: protein–protein complex structure by SAXS and computational docking , 2015, Nucleic Acids Res..

[64]  William R. Taylor An algorithm to parse segment packing in predicted protein contact maps , 2016, Algorithms for Molecular Biology.

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

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

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

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

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

[70]  Sandor Vajda,et al.  ClusPro: an automated docking and discrimination method for the prediction of protein complexes , 2004, Bioinform..

[71]  Faruck Morcos,et al.  Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis , 2016, Proceedings of the National Academy of Sciences.

[72]  Matteo Marsili,et al.  Identifying relevant positions in proteins by Critical Variable Selection. , 2015, Molecular bioSystems.

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

[74]  Olga Mayans,et al.  Structural advances on titin: towards an atomic understanding of multi-domain functions in myofilament mechanics and scaffolding. , 2015, Biochemical Society transactions.

[75]  Oliver Brock,et al.  Blind Evaluation of Hybrid Protein Structure Analysis Methods based on Cross-Linking , 2016, Trends in biochemical sciences.

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

[77]  Gunnar Jeschke,et al.  Combining NMR and EPR to Determine Structures of Large RNAs and Protein-RNA Complexes in Solution. , 2015, Methods in enzymology.

[78]  Florencio Pazos,et al.  Studying the co-evolution of protein families with the Mirrortree web server , 2010, Bioinform..

[79]  Dmitrij Frishman,et al.  Accurate prediction of helix interactions and residue contacts in membrane proteins. , 2016, Journal of structural biology.

[80]  J. Meiler,et al.  Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX. , 2016, Journal of structural biology.

[81]  Mieczyslaw Torchala,et al.  SwarmDock: a server for flexible protein-protein docking , 2013, Bioinform..

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

[83]  Leighton Coates,et al.  The 1.1 Å resolution structure of a periplasmic phosphate-binding protein from Stenotrophomonas maltophilia: a crystallization contaminant identified by molecular replacement using the entire Protein Data Bank. , 2016, Acta crystallographica. Section D, Structural biology.

[84]  Chan-seok Jeong,et al.  Structure-based Markov random field model for representing evolutionary constraints on functional sites , 2016, BMC Bioinformatics.

[85]  Ruth Nussinov,et al.  Efficient Unbound Docking of Rigid Molecules , 2002, WABI.

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

[87]  Michael T. Laub,et al.  Rewiring the Specificity of Two-Component Signal Transduction Systems , 2008, Cell.

[88]  Ron O. Dror,et al.  Crystal Structure of a Full-Length Human Tetraspanin Reveals a Cholesterol-Binding Pocket , 2016, Cell.

[89]  Pierre Baldi,et al.  Three-stage prediction of protein ?-sheets by neural networks, alignments and graph algorithms , 2005, ISMB.

[90]  Jing Yang,et al.  R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter , 2016, Bioinform..

[91]  Sean R. Eddy,et al.  Hidden Markov model speed heuristic and iterative HMM search procedure , 2010, BMC Bioinformatics.

[92]  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.

[93]  Gunnar Jeschke,et al.  EPR-aided approach for solution structure determination of large RNAs or protein–RNA complexes , 2014, Nature Communications.

[94]  Jose M. Duarte,et al.  Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts , 2015, Bioinform..

[95]  P. Barth,et al.  Evolutionary-guided de novo structure prediction of self-associated transmembrane helical proteins with near-atomic accuracy , 2015, Nature Communications.

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

[97]  Pablo Chacón,et al.  FRODOCK 2.0: fast protein-protein docking server , 2016, Bioinform..

[98]  Kathrin Meindl,et al.  Exploiting tertiary structure through local folds for crystallographic phasing , 2013, Nature Methods.

[99]  Andrej Sali,et al.  Modeling of proteins and their assemblies with the integrative modeling platform. , 2011, Methods in molecular biology.

[100]  David T. Jones,et al.  Accurate contact predictions using covariation techniques and machine learning , 2015, Proteins.

[101]  Oliver F. Lange,et al.  Determination of solution structures of proteins up to 40 kDa using CS-Rosetta with sparse NMR data from deuterated samples , 2012, Proceedings of the National Academy of Sciences.

[102]  Cathy H. Wu,et al.  Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning. , 2016, Methods.

[103]  Itay Mayrose,et al.  ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules , 2016, Nucleic Acids Res..

[104]  Oliver F. Lange,et al.  NMR Structure Determination for Larger Proteins Using Backbone-Only Data , 2010, Science.

[105]  Matthias J. Brunner,et al.  Atomic accuracy models from 4.5 Å cryo-electron microscopy data with density-guided iterative local refinement , 2015, Nature Methods.

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

[107]  Sergey Ovchinnikov,et al.  Structure of a bd oxidase indicates similar mechanisms for membrane-integrated oxygen reductases , 2016, Science.

[108]  Michael J E Sternberg,et al.  The Phyre2 web portal for protein modeling, prediction and analysis , 2015, Nature Protocols.

[109]  Zhiyong Wang,et al.  Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning , 2013, Bioinform..