Accurate contact predictions using covariation techniques and machine learning

Proteins: Structure, Function and Bioinformatics

Volumn 84, Issue S1, 2016, Pages 145-151

  Kosciolek, Tomasz ^a   Jones, David T ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

ab initio prediction; amino acid covariation; CASP; protein structure prediction; residue residue contact prediction

Indexed keywords

PROTEIN;

AMINO ACID SEQUENCE; ARTIFICIAL NEURAL NETWORK; BACTERIUM; BIOLOGY; CHEMISTRY; COMPUTER SIMULATION; HUMAN; INTERNET; MACHINE LEARNING; MOLECULAR MODEL; PROCEDURES; PROTEIN DATABASE; PROTEIN DOMAIN; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; SEQUENCE ALIGNMENT; SOFTWARE; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; VIRUS;

AMINO ACID SEQUENCE; BACTERIA; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, PROTEIN; HUMANS; INTERNET; MACHINE LEARNING; MODELS, MOLECULAR; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROTEIN FOLDING; PROTEIN INTERACTION DOMAINS AND MOTIFS; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ALIGNMENT; SOFTWARE; VIRUSES;

EID: 84939515755     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24863     Document Type: Article

References (27)

1. Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C. Protein 3D structure computed from evolutionary sequence variation. PLoS One 2011; 6:e28766
2. Weigt M, White RA, Szurmant H, Hoch JA, Hwa T. Identification of direct residue contacts in protein-protein interaction by message passing. Proc Natl Acad Sci USA 2009; 106:67–72.
3. Morcos F, Jana B, Hwa T, Onuchic JN. Coevolutionary signals across protein lineages help capture multiple protein conformations. Proc Natl Acad Sci USA 2013; 110:20533–20538.
4. Jones DT, Buchan DWA, Cozzetto D, Pontil M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 2012; 28:184–190.
5. Kamisetty H, Ovchinnikov S, Baker D. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era. Proc Natl Acad Sci USA 2013; 110:15674–15679.
6. Ekeberg M, Lövkvist C, Lan Y, Weigt M, Aurell E. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys Rev E 2013; 87:012707
7. Morcos F, Pagnani A, Lunt B, Bertolino A, Marks DS, Sander C, Zecchina R, Onuchic JN, Hwa T, Weigt M. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Natl Acad Sci USA 2011; 108:E1293–E1301.
8. Monastyrskyy B, D'Andrea D, Fidelis K, Tramontano A, Kryshtafovych A. Evaluation of residue–residue contact prediction in CASP10. Proteins 2014; 82(Suppl 2):138–153.
9. Kim DE, DiMaio F, Yu-Ruei Wang R, Song Y, Baker D. One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins 2014; 82(Suppl 2):208–218.
10. Tetchner S, Kosciolek T, Jones DT. Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction. Bio-Algorithms Med-Systems 2014; 10:243–254.
11. Cheng J, Baldi P. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 2007; 8:113
12. Jones DT, Singh T, Kosciolek T, Tetchner S. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 2014; 31:999–1006.
13. Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 2012; 9:173–175.
14. Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 2011; 39:W29–W37.
15. Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B. FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics 2014; 15:85
16. Seemayer S, Gruber M, Söding J. CCMpred–fast and precise prediction of protein residue–residue contacts from correlated mutations. Bioinformatics 2014; 30:3128–3130.
17. Dunn SD, Wahl LM, Gloor GB. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 2008; 24:333–340.
18. Skwark MJ, Raimondi D, Michel M, Elofsson A. Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput Biol 2014; 10:e1003889
19. Ekeberg M, Hartonen T, Aurell E. Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences. J Comput Phys 2014; 276:341–356.
20. Kosciolek T, Jones DT. De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS One 2014; 9:e92197
21. Jones DT. Successful ab initio prediction of the tertiary structure of NK-lysin using multiple sequences and recognized supersecondary structural motifs. Proteins 1997; 29(Suppl 1):185–191.
22. Jones DT. Predicting novel protein folds by using FRAGFOLD. Proteins 2001; 45(Suppl 5):127–132.
23. Jones DT, Bryson K, Coleman A, McGuffin LJ, Sadowski MI, Sodhi JS, Ward JJ. Prediction of novel and analogous folds using fragment assembly and fold recognition. Proteins 2005; 61(Suppl 7):143–151.
24. Nugent T, Jones DT. Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc Natl Acad Sci USA 2012; 109:E1540–E1547.
25. Nugent T, Jones DT. Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics 2009; 10:159
26. Taylor WR, Hamilton RS, Sadowski MI. Prediction of contacts from correlated sequence substitutions. Curr Opin Struct Biol 2013; 23:473–479.
27. Marks DS, Hopf TA, Sander C. Protein structure prediction from sequence variation. Nat Biotechnol 2012; 30:1072–1080.