Ranking the quality of protein structure models using sidechain based network properties

F1000Research

Volumn 3, Issue , 2014, Pages

  Vishveshwara, Saraswathi ^a   Ghosh, Soma ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; HYDROGEN BOND; MOLECULAR MODEL; PROCESS DEVELOPMENT; PROTEIN DETERMINATION; PROTEIN STRUCTURE; PROTEIN STRUCTURE NETWORK; QUALITY CONTROL; STRUCTURAL HOMOLOGY; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE; WEB BROWSER;

EID: 84915775914     PISSN: 20461402     EISSN: 1759796X     Source Type: Journal    
DOI: 10.12688/f1000research.3-17.v1     Document Type: Article

References (47)

1. Anfinsen C: Principles that govern the folding of protein chains. Science. 1973;181(4096):223-230. 4124164 10.1126/science.181.4096.223
2. Daggett V Fersht A: The present view of the mechanism of protein folding. Nat Rev Mol Cell Biol. 2003;4(6):497-502. 12778129 10.1038/nrm1126
3. Chan HS Dill KA: The protein folding problem. Phys Today. 1993;46(2):24. 10.1063/1.881371
4. Fersht AR: From the first protein structures to our current knowledge of protein folding: delights and scepticisms. Nat Rev Mol Cell Biol. 2008;9(8):650-654. 18578032 10.1038/nrm2446
5. Karplus M: Behind the folding funnel diagram. Nat Chem Biol. 2011;7(7):401-404. 21685880 10.1038/nchembio.565
6. Rose GD Fleming PJ Banavar JR: A backbone-based theory of protein folding. Proc Natl Acad Sci U S A. 2006;103(45):16623-16633. 17075053 10.1073/pnas.0606843103 1636505
7. Dyson HJ Wright PE Scheraga HA: The role of hydrophobic interactions in initiation and propagation of protein folding. Proc Natl Acad Sci U S A. 2006;103(35):13057-13061. 16916929 10.1073/pnas.0605504103 1559752
8. Kellis JT Jr Nyberg K Sali D: Contribution of hydrophobic interactions to protein stability. Nature. 1988;333(6175):784-786. 3386721 10.1038/333784a0
9. Burley S Petsko G: Aromatic-aromatic interaction: a mechanism of protein structure stabilization. Science. 1985;229(4708):23-28. 3892686 10.1126/science.3892686
10. Gilis D: Protein decoy sets for evaluating energy functions. J Biomol Struct Dyn. 2004;21(6):725-736. 15106995 10.1080/07391102.2004.10506963
11. Samudrala R Levitt M: Decoys 'R' Us: a database of incorrect conformations to improve protein structure prediction. Protein Sci. 2000;9(7):1399-1401. 10933507 10.1110/ps.9.7.1399 2144680
12. Tsai J Bonneau R Morozov AV: An improved protein decoy set for testing energy functions for protein structure prediction. Proteins. 2003;53(1):76-87. 12945051 10.1002/prot.10454
13. Huang ES Subbiah S Tsai J: Using a hydrophobic contact potential to evaluate native and near-native folds generated by molecular dynamics simulations. J Mol Biol. 1996;257(3):716-725. 8648635 10.1006/jmbi.1996.0196
14. Zhang J Zhang Y: A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS One. 2010;5(10):e15386. 21060880 10.1371/journal.pone.0015386 2965178
15. Wu S Skolnick J Zhang Y: Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 2007;5:17. 17488521 10.1186/1741-7007-5-17 1878469
16. Park B Levitt M: Energy functions that discriminate X-ray and near-native folds from well-constructed decoys. J Mol Biol. 1996;258(2):367-392. 8627632 10.1006/jmbi.1996.0256
17. Rahat O Alon U Levy Y: Understanding hydrogen-bond patterns in proteins using network motifs. Bioinformatics. 2009;25(22):2921-2928. 19767299 10.1093/bioinformatics/btp541
18. Atilgan AR Akan P Baysal C: Small-world communication of residues and significance for protein dynamics. Biophys J. 2004;86(1 Pt 1):85-91. 14695252 10.1016/S0006-3495(04)74086-2 1303839
19. Vishveshwara S Brinda K Kannan N: Protein structure: insights from graph theory. J Theor Comput Chem. 2002;1(01):187-211. 10.1142/S0219633602000117
20. Kannan N Vishveshwara S: Identification of side-chain clusters in protein structures by a graph spectral method. J Mol Biol. 1999;292(2):441-464. 10493887 10.1006/jmbi.1999.3058
21. Sathyapriya R Vijayabaskar M Vishveshwara S: Insights into Protein-DNA Interactions through structure network analysis. PLoS Comput Biol. 2008;4(9):e1000170. 18773096 10.1371/journal.pcbi.1000170 2518215
22. Deb D Vishveshwara S Vishveshwara S: Understanding protein structure from a percolation perspective. Biophys J. 2009;97(6):1787-1794. 19751685 10.1016/j.bpj.2009.07.016 2749797
23. Bhattacharyya M Vishveshwara S: Probing the allosteric mechanism in pyrrolysyl-tRNA synthetase using energy-weighted network formalism. Biochemistry. 2011;50(28):6225-6236. 21650159 10.1021/bi200306u
24. Brinda K Vishveshwara S: A network representation of protein structures: implications for protein stability. Biophys J. 2005;89(6):4159-4170. 16150969 10.1529/biophysj.105.064485 1366981
25. Brinda KV Kannan N Vishveshwara S: Analysis of homodimeric protein interfaces by graph-spectral methods. Protein Eng. 2002;15(4):265-277. 11983927 10.1093/protein/15.4.265
26. Kannan N Vishveshwara S: Aromatic clusters: a determinant of thermal stability of thermophilic proteins. Protein Eng. 2000;13(11):753-761. 11161106 10.1093/protein/13.11.753
27. Noble WS: What is a support vector machine? Nat Biotechnol. 2006;24(12):1565-1567. 17160063 10.1038/nbt1206-1565
28. Park KJ Kanehisa M: Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics. 2003;19(13):1656-1663. 12967962 10.1093/bioinformatics/btg222
29. Hua S Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics. 2001;17(8):721-728. 11524373 10.1093/bioinformatics/17.8.721
30. Khan J Wei JS Ringner M: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7(6):673-679. 11385503 10.1038/89044 1282521
31. Guyon I Weston J Barnhill S: Gene selection for cancer classification using support vector machines. Machine Learn. 2002;46(1-3):389-422. 10.1023/A:1012487302797
32. Furey TS Cristianini N Duffy N: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16(10):906-914. 11120680 10.1093/bioinformatics/16.10.906
33. Cai YD Liu XJ Xu Xb: Prediction of protein structural classes by support vector machines. Comput Chem. 2002;26(3):293-296. 11868916
34. Koike A Takagi T: Prediction of protein-protein interaction sites using support vector machines. Protein Eng Des Sel. 2004;17(2):165-173. 15047913 10.1093/protein/gzh020
35. Bradford JR Westhead DR: Improved prediction of protein-protein binding sites using a support vector machines approach. Bioinformatics. 2005;21(8):1487-1494. 15613384 10.1093/bioinformatics/bti242
36. Kryshtafovych A Fidelis K: Protein structure prediction and model quality assessment. Drug Discovery Today. 2009;14(7):386-393. 19100336 10.1016/j.drudis.2008.11.010 2808711
37. Dong Q Chen Y Zhou S: A machine learning-based method for protein global model quality assessment. Int J Gen Syst. 2011;40(04):417-425. 10.1080/03081079.2010.544869
38. Chatterjee S Bhattacharyya M Vishveshwara S: Network properties of protein-decoy structures. J Biomol Struct Dyn. 2012;29(6):606-622. 22545992 10.1080/07391102.2011.672625
39. Chatterjee S Ghosh S Vishveshwara S: Network properties of decoys and CASP predicted models: A comparison with native protein structures. Mol Biosyst. 2013;9(7):1774-1788. 23694935 10.1039/c3mb70157c
40. Wang G Dunbrack RL: PISCES: a protein sequence culling server. Bioinformatics. 2003;19(12):1589-1591. 12912846 10.1093/bioinformatics/btg224
41. McDonald I Naylor D Jones D: HBPLUS computer program. Department of Biochemistry and Molecular Biology, University College, London, UK.1993. Reference Source
42. Chang CC Lin CJ: LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011;2(3):27. 10.1145/1961189.1961199
43. Fan RE Chang KW Hsieh CJ: LIBLINEAR: A library for large linear classification. J Machine Learn Res. 2008;9:1871-1874. 10.1145/1390681.1442794
44. Leiserson CE Rivest RL Stein C: Introduction to algorithms. The MIT press.2001. Reference Source
45. Adamcsek B Palla G Farkas IJ: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics. 2006;22(8):1021-1023. 16473872 10.1093/bioinformatics/btl039
46. Soffer SN Vázquez A: Network clustering coefficient without degree-correlation biases. Phys Rev E Stat Nonlin Soft Matter Phys. 2005;71(5 Pt 2):057101. 16089694 10.1103/PhysRevE.71.057101
47. Ghosh S Vishveshwara S: Protein Structure Network: Quality Assessment (PSN-QA). Figshare.2014. Data Source