Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta

Proteins: Structure, Function and Bioinformatics

Volumn 84, Issue , 2016, Pages 67-75

  Ovchinnikov, Sergey ^a,b   Kim, David E ^b   Wang, Ray Yu Ruei ^a,b   Liu, Yuan ^a,b   Dimaio, Frank ^a,b   Baker, David ^a,b  

^a UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Ab initio prediction; Coevolution; Contact prediction; Protein structure prediction; Rosetta

Indexed keywords

ESCHERICHIA COLI PROTEIN; YAAA PROTEIN, E COLI;

AMINO ACID SEQUENCE; BIOLOGY; CHEMISTRY; DIRECTED MOLECULAR EVOLUTION; ESCHERICHIA COLI; HUMAN; INTERNET; MOLECULAR MODEL; PROCEDURES; PROTEIN DOMAIN; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; SEQUENCE ALIGNMENT; SOFTWARE; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; X RAY CRYSTALLOGRAPHY;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; CRYSTALLOGRAPHY, X-RAY; DIRECTED MOLECULAR EVOLUTION; ESCHERICHIA COLI; ESCHERICHIA COLI PROTEINS; HUMANS; INTERNET; MODELS, MOLECULAR; MODELS, STATISTICAL; PROTEIN FOLDING; PROTEIN INTERACTION DOMAINS AND MOTIFS; PROTEIN STRUCTURE, SECONDARY; SEQUENCE ALIGNMENT; SOFTWARE;

EID: 84959378045     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24974     Document Type: Article

References (39)

1. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 2004;32(Web Server issue):W526–531.
2. Simons KT, Ruczinski I, Kooperberg C, Fox BA, Bystroff C, Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 1999;34:82–95.
3. Conway P, Tyka MD, DiMaio F, Konerding DE, Baker D. Relaxation of backbone bond geometry improves protein energy landscape modeling. Protein Sci 2014;23:47–55.
4. Balakrishnan S, Kamisetty H, Carbonell JG, Lee SI, Langmead CJ. Learning generative models for protein fold families. Proteins 2011; 79:1061–1078.
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. Ovchinnikov S, Kamisetty H, Baker D. Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information. eLife 2014;3:e02030.
7. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 2011;8:785–786.
8. Soding J. Protein homology detection by HMM-HMM comparison. Bioinformatics 2005;21:951–960.
9. Yang Y, Faraggi E, Zhao H, Zhou Y. Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 2011;27:2076–2082.
10. Kallberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, Xu J. Template-based protein structure modeling using the RaptorX web server. Nat Protoc 2012;7:1511–1522.
11. Song Y, DiMaio F, Wang RY, Kim D, Miles C, Brunette T, Thompson J, Baker D. High-resolution comparative modeling with RosettaCM. Structure 2013;21:1735–1742.
12. Siew N, Elofsson A, Rychlewski L, Fischer D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 2000;16:776–785.
13. Kim DE, Chivian D, Malmstrom L, Baker D. Automated prediction of domain boundaries in CASP6 targets using Ginzu and Rosetta-DOM. Proteins 2005;61(Suppl 7):193–200.
14. Doolittle RF. Of URFs and ORFs: a primer on how to analyze derived amino acid sequences. Mill Valley, California: University Science Books; 1986.
15. Bradley P, Misura KM, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science 2005;309: 1868–1871.
16. Remmert M, Biegert A, Hauser A, Soding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 2012;9:173–175.
17. Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol 2011;7:e1002195.
18. Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 2015;31:926–932.
19. Gront D, Kulp DW, Vernon RM, Strauss CE, Baker D. Generalized fragment picking in Rosetta: design, protocols and applications. PLoS One 2011;6:e23294.
20. Zhou H, Zhou Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 2005;58:321–328.
21. Faraggi E, Xue B, Zhou Y. Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 2009;74:847–856.
22. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195–202.
23. Raman S, Vernon R, Thompson J, Tyka M, Sadreyev R, Pei J, Kim D, Kellogg E, DiMaio F, Lange O, Kinch L, Sheffler W, Kim BH, Das R, Grishin NV, Baker D. Structure prediction for CASP8 with all-atom refinement using Rosetta. Proteins 2009;77(Suppl 9):89–99.
24. Thompson J, Baker D. Incorporation of evolutionary information into Rosetta comparative modeling. Proteins 2011;79:2380–2388.
25. Anderson DP. BOINC: a system for public-resource computing and storage. In: IEEE/ACM International Workshop on Grid Computing. Pittsburgh, PA; 2004. pp 4-10.
26. Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT. The DIS-OPRED server for the prediction of protein disorder. Bioinformatics 2004;20:2138–2139.
27. Wollacott AM, Zanghellini A, Murphy P, Baker D. Prediction of structures of multidomain proteins from structures of the individual domains. Protein Sci 2007;16:165–175.
28. Zhou H, Skolnick J. GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophys J 2011;101:2043–2052.
29. Zemla A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 2003;31:3370–3374.
30. Xu J, Zhang Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 2010;26:889–895.
31. Kryshtafovych A, Fidelis K, Tramontano A. Evaluation of model quality predictions in CASP9. Proteins 2011;79(Suppl10):91–106.
32. Kryshtafovych A, Barbato A, Fidelis K, Monastyrskyy B, Schwede T, Tramontano A. Assessment of the assessment: evaluation of the model quality estimates in CASP10. Proteins 2014;82(Suppl2):112–126.
33. Ray A, Lindahl E, Wallner B. Improved model quality assessment using ProQ2. BMC Bioinformatics 2012;13:224.
34. Haas J, Roth S, Arnold K, Kiefer F, Schmidt T, Bordoli L, Schwede T. The protein model portal–a comprehensive resource for protein structure and model information. Database 2013;2013:bat031.
35. Ovchinnikov S, Kinch L, Park H, Liao Y, Pei J, Kim DE, Kamisetty H, Grishin N, Baker D. Large-scale determination of previously unsolved protein structures using evolutionary information. eLife 2015;4:e09248.
36. Romero PA, Arnold FH. Exploring protein fitness landscapes by directed evolution. Nat Rev Mol Cell Biol 2009;10:866–876.
37. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol 2008;26:1135–1145.
38. Bork P, Bowler C, de Vargas C, Gorsky G, Karsenti E, Wincker P. Tara Oceans. Tara Oceans studies plankton at planetary scale. Introduction. Science 2015;348:873.
39. Sunagawa S, Karsenti E, Bowler C, Bork P. Computational ecosystems biology in Tara Oceans: translating data into knowledge. Mol Syst Biol 2015;11:809.