PAIRpred: Partner-specific prediction of interacting residues from sequence and structure

Proteins: Structure, Function and Bioinformatics

Volumn 82, Issue 7, 2014, Pages 1142-1155

  Afsar Minhas, Fayyaz ul Amir ^a   Geiss, Brian J ^b   Ben Hur, Asa ^a  

^a Department of Computer Science   (United States)

^b Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Protein binding site prediction; Protein interface prediction

Indexed keywords

DOCKING PROTEIN; NONSTRUCTURAL PROTEIN 1; PROTEIN; PROTEIN BINDING;

AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; CONFORMATIONAL TRANSITION; CONTROLLED STUDY; HUMAN; KERNEL METHOD; MACHINE LEARNING; MOLECULAR DOCKING; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; PROTEIN TARGETING; SUPPORT VECTOR MACHINE; X RAY CRYSTALLOGRAPHY; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; METABOLISM; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE;

INFLUENZA A VIRUS;

BINDING SITES; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, MOLECULAR; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 84902195508     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.24479     Document Type: Article

References (54)

1. Ezkurdia I, Bartoli L, Fariselli P, Casadio R, Valencia A, Tress ML. Progress and challenges in predicting protein-protein interaction sites. Brief Bioinform 2009;10:233-246.
2. Keskin O, Gursoy A, Ma B, Nussinov R. Principles of protein-protein interactions: what are the preferred ways for proteins to interact? Chem Rev 2008;108:1225-1244.
3. Changeux J-P, Edelstein S. Conformational selection or induced-fit? 50 years of debate resolved. F1000 Biol Reports 2011;3:19.
4. Gunasekaran K, Nussinov R. How different are structurally flexible and rigid binding sites? Sequence and structural features discriminating proteins that do and do not undergo conformational change upon ligand binding. J Mol Biol 2007;365:257-273.
5. Wass MN, David A, Sternberg MJ. Challenges for the prediction of macromolecular interactions. Curr Opin Struct Biol 2011;21:382-390.
6. Zhou H-X, Qin S. Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics 2007;23:2203-2209.
7. Ofran Y. Prediction of protein interaction sites. In: Nussinov R, Schreiber G, editors. Computational protein-protein interaction. Boca Raton: CRC Press; 2009. pp 167-184.
8. Leis S, Schneider S, Zacharias M. In silico prediction of binding sites on proteins. Curr Med Chem 2010;2010(17):1550-1562.
9. Ahmad S, Mizuguchi K. Partner-aware prediction of interacting residues in protein-protein complexes from sequence data. PLoS One 2011;6.
10. Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem AA, Aflalo C, Vakser IA. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci 1992;89:2195-2199.
11. Pierce BG, Hourai Y, Weng Z. Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS One 2011;6:e24657.
12. Duhovny D, Nussinov R, Wolfson HJ. Efficient unbound docking of rigid molecules. In: Proceedings of the 2nd workshop on algorithms in bioinformatics (WABI). Notes in computer science 2452. Berlin Heidelberg: Springer Verlag; 2002. pp 85-200.
13. Janin J. Docking predictions of protein-protein interactions and their assessment: the CAPRI experiment. In: Roterman-Konieczna I, editor. Identification of ligand binding site and protein-protein interaction area, no. 8 in focus on structural biology. Netherlands: Springer; 2013. pp 87-104.
14. Gray JJ, Moughon S, Wang C, Schueler-Furman O, Kuhlman B, Rohl CA, Baker D. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J Mol Biol 2003;331(1):281-299.
15. Kundrotas PJ, Zhu Z, Janin J, Vakser IA. Templates are available to model nearly all complexes of structurally characterized proteins. Proc Natl Acad Sci 2012;109:9438-9441.
16. de Vries SJ, van Dijk ADJ, Krzeminski M, van Dijk M, Thureau A, Hsu V, Wassenaar T, Bonvin AMJJ. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 2007;69:726-733.
17. Schueler-Furman O, Wang C, Baker D. Progress in protein-protein docking: atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side-chain flexibility. Proteins 2005;60(2):187-194.
18. Bordner AJ, Gorin AA. Protein docking using surface matching and supervised machine learning. Proteins 2007;68(2):488-502.
19. Martin O, Schomburg D. Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines. Proteins 2008;70(4):1367-1378.
20. Huang B, Schroeder M. Using protein binding site prediction to improve protein docking. Gene 2008;422:14-21.
21. Tuncbag N, Gursoy A, Keskin O. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Phys Biol 2011;8:035006.
22. Vreven T, Hwang H, Pierce BG, Weng Z. Evaluating template-based and template-free protein-protein complex structure prediction. Brief Bioinform 2013: bbt047.
23. Wang H, Segal E, Ben-Hur A, Li Q-R, Vidal M, Koller D. InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale. Genome Biol 2007;8(9):R192.
24. Brown CD, Davis HT. Receiver operating characteristics curves and related decision measures: a tutorial. Chemom Intell Lab Syst 2006;80:24-38.
25. Hwang H, Vreven T, Janin J, Weng Z. Protein-protein docking benchmark version 4.0. Proteins 2010;78(15):3111-3114.
26. Zhang QC, Deng L, Fisher M, Guan J, Honig B, Petrey D. PredUs: a web server for predicting protein interfaces using structural neighbors. Nucleic Acids Res 2011;39:W283-W287.
27. Andreeva A, Howorth D, Brenner SE, Hubbard TJP, Chothia C, Murzin AG. SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 2004;32:D226-229.
28. Hwang H, Pierce B, Mintseris J, Janin J, Weng Z. Protein-protein docking benchmark version 3.0. Proteins 2008;73(3):705-709.
29. Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995;23:566-579.
30. Sanner M, Olson A, Spehner J-C. Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 1996;38(3):305-320.
31. Hamelryck T. An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins 2005;59(1):38-48.
32. Cock, Peter JA, Tiago Ao, Jeffrey T., Chang, Brad A., Chapman, Cymon J., Cox Andrew Dalke, Iddo Friedberg et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009;25:1422-1423.
33. Pintar A, Carugo O, Pongor S. CX, an algorithm that identifies protruding atoms in proteins. Bioinformatics (Oxford, England) 2002;18:980-984.
34. Mihel J, iki M, Tomi S, Jeren B, Vlahoviek K. PSAIA-protein structure and interaction analyzer. BMC Struct Biol 2008;8:21.
35. Altschul SF, Madden TL, Schffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-3402.
36. McGinnis S, Madden TL. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 2004;32:W20-W25.
37. Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y. SPINE x: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 2012;33:259-267.
38. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273-297.
39. Ben-Hur A, Ong CS, Sonnenburg S, Schlkopf B, Rtsch G. Support vector machines and kernels for computational biology. PLoS Comput Biol 2008;4:e1000173.
40. Ben-Hur A, Noble WS. Kernel methods for predicting protein-protein interactions. Bioinformatics 2005;21:i38-i46.
41. Vert J-P, Qiu J, Noble W. A new pairwise kernel for biological network inference with support vector machines. BMC Bioinformatics 2007;8:S8.
42. Bar-hillel A, Weinshall D. Boosting margin based distance functions for clustering. In: Proceedings of the twenty-first international conference on machine learning; 2004. pp 393-400.
43. Brunner C, Fischer A, Luig K, Thies T. Pairwise support vector machines and their application to large scale problems. J Machine Learning Res 2012;13:2279-2292.
44. Brunner C, Fischer A, Luig K, Thies T. Pairwise kernels, support vector machines, and the application to large scale problems. Tech. rep. Technische Universitat Dresden Institut fur Numerische Mathematik; 2011.
45. Haykin SS. Neural networks a comprehensive foundation, 2nd ed. Upper Saddle River, NJ: Prentice Hall; 1999.
46. Ben-Hur A. PyML: machine learning using python (http://pyml.sourceforge.net/); December 2012.
47. Lensink MF, Wodak SJ. Docking and scoring protein interactions: CAPRI 2009. Proteins 2010;78(15):3073-3084.
48. Guan R, Ma L-C, Leonard PG, Amer BR, Sridharan H, Zhao C, Krug RM, Montelione GT. Structural basis for the sequence-specific recognition of human ISG15 by the NS1 protein of influenza b virus. Proc Natl Acad Sci 2011;108:13468-13473.
49. Roy, Ambrish, Alper Kucukural and Yang Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature protocols 2010;5:725-738.
50. Yin C, Khan JA, Swapna GVT, Ertekin A, Krug RM, Tong L, Montelione GT. Conserved surface features form the double-stranded RNA binding site of non-structural protein 1 (NS1) from influenza a and b viruses. J Biol Chem 2007;282:20584-20592.
51. Narasimhan J. Crystal structure of the interferon-induced ubiquitin-like protein ISG15. J Biol Chem 2005;280:27356-27365.
52. 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.
53. 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.
54. Marsh J, Teichmann S. Relative solvent accessible surface area predicts protein conformational changes upon binding. Structure 2011;19:859-867.