RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method

Protein and Peptide Letters

Volumn 19, Issue 1, 2012, Pages 50-56

  Pugalenthi, Ganesan ^a,b   Kandaswamy, Krishna Kumar ^c   Chou, Kuo Chen ^d   Vivekanandan, Saravanan ^a   Kolatkar, Prasanna ^a  

^a GENOME INSTITUTE OF SINGAPORE   (Singapore)

^b KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^c UNIVERSITY OF LÜBECK   (Germany)

^d GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

Accessible surface area; Conserved residue; Functional residue; Hydrophobic core; Protein interface; Protein structure prediction

Indexed keywords

SOLVENT; THIOREDOXIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; HYDROPHILICITY; HYDROPHOBICITY; MATHEMATICAL MODEL; PHYSICAL CHEMISTRY; PREDICTION; PROTEIN DATABASE; PROTEIN DETERMINATION; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; QUALITY CONTROL; RANDOM FOREST; RESIDUE ANALYSIS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; SURFACE PROPERTY;

EID: 84858167590     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986612798472875     Document Type: Article

References (43)

1. Chothia, C.; One thousand families for the molecular biologist. Nature, 1992,357, 543-544.
2. Whisstock, J.C.; Lesk, A.M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys., 2003, 36(3), 307-340. (Pubitemid 38268660)
3. Pugalenthi, G.; Kumar, K. K.; Suganthan, P.N.; Gangal, R. Identi-fication of catalytic residues from protein structure using support vector machine with sequence and structural features. Biochem. Biophys. Res. Commun., 2008, 367, 630-634.
4. Rost, B.; Yachdav, G.; Liu, J. The predict protein server. Nucleic Acids Res., 2004, 32, W321-W326.
5. Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 2008, 9, 40.
6. Brenner, S.E. A tour of structural genomics. Nat. Rev. Genet., 2001, 2(10), 801-809. (Pubitemid 33685577)
7. McGuffin, L, J.; Bryson, K.; Jones, D.T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, 16(4), 404-405. (Pubitemid 30417087)
8. Petersen, B.; Petersen T.N.; Andersen, P.; Nielsen, M.; Lunde-gaard, C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol., 2009, 9, 51.
9. Cuff, J.A.; Clamp, M.E.; Siddiqui, A.S.; Finlay, M.; Barton, G.J. JPred: a consensus secondary structure prediction server. Bioinfor-matics, 1998, 14(10), 892-893. (Pubitemid 29041548)
10. Frishman, D.; Argos, P. 75% accuracy in protein secondary structure prediction. Proteins, 1997, 27, 329-335. (Pubitemid 27149285)
11. Pugalenthi, G.; Tang, K.; Suganthan, P.N.; Chakrabarti, S. Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble. Bioinformatics, 2009, 25(2), 204-210.
12. Ahmad, S.; Gromiha, M.M.; Sarai, A. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural Information. Bioinformatics, 2004, 20, 477-486 (Pubitemid 38344327)
13. Chothia, C. The nature of the accessible and buried surfaces in proteins. J. Mol. Biol., 1976, 105, 1-12.
14. Farber, P.J.; Mittermaier, A. Side chain burial and hydrophobic core packing in protein folding transition states. Protein Sci., 2008, 17(4), 644-651. (Pubitemid 351480858)
15. Ventura, S.; Serrano, L. Designing proteins from the inside out. Proteins, 2004, 56(1), 1-10. (Pubitemid 38720833)
16. Wodak, S.J.; Mendez, R. Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications. Curr. Opin. Struct. Biol., 2004. 14, 242-249. (Pubitemid 38490791)
17. Pugalenthi, G.; Archunan, G.; Sowdhamini, R. DIAL: a web-based server for the automatic identification of structural domains in proteins. Nucleic Acids Res., 2005, 33, W130-132. (Pubitemid 44529894)
18. Andrade, M.A.; O'Donoghue, S. I.; Rost, B. Adaptation of protein surfaces to subcellular location. J. Mol. Biol., 1998, 276, 517-525. (Pubitemid 28085337)
19. Ehrlich, L.; Reczko, M.; Bohr, H.; Wade, R.C. Prediction of protein hydration sites from sequence by modular neural networks. Protein Eng., 1998, 11(1), 11-19. (Pubitemid 28140476)
20. Chen, H.; Zhou, H.X. Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucleic Acids Res., 2005, 33(10), 3193-3199. (Pubitemid 41194463)
21. Mizuguchi, K.; Deane, C.M.; Blundell, T.L.; Johnson, M.S.; Over-ington, J.P. JOY: protein sequence-structure representation and analysis. Bioinformatics, 1998, 14, 617-623. (Pubitemid 28461583)
22. Kabsch, W.; Sander, C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bond and geometrical features. Biopolymers, 1983, 22, 2577-2637.
23. NACCESS, Computer program, Department of Biochemistry and Molecular Biology [http://wolf.bi.umist.ac.uk/unix/naccess.html]
24. Fraczkiewicz, R.; Braun, W. Exact and efficient analytical calculation of the accessible surface areas and their gradients for macro-molecules. J. Comp. Chem., 1998, 19, 319-333. (Pubitemid 128592649)
25. Tsodikov, O. V.; Record, M. T. Jr.; Sergeev, Y. V. A novel computer program for fast exact calculation of accessible and molecular surface areas and average surface curvature. J. Comput. Chem., 2002, 23, 600-609. (Pubitemid 34312083)
26. Ahmad, S.; Gromiha, M.; Sarai, A. RVP-net: online prediction of real valued accessible surface area of proteins from single sequences. Bioinformatics, 2003, 19(14), 1849-1851. (Pubitemid 37220479)
27. Rost, B.; Sander, C. Conservation and prediction of solvent accessibility in protein families. Proteins, 1994, 20, 216-226. (Pubitemid 24378913)
28. Ahmad, S.; Gromiha, M.M. NETASA: Neural network based prediction of solvent accessibility. Bioinformatics, 2002, 18, 819-824. (Pubitemid 34727642)
29. Ahmad, S.; Gromiha, M. M.; Sarai. A. Real-value prediction of solvent accessibility from amino acid sequence. Proteins, 2003, 50, 629-635. (Pubitemid 36330487)
30. Pollastri, G.; Baldi, P.; Fariselli, P.; Casadio, R. Prediction of coordination number and relative solvent accessibility. Proteins, 2002, 47, 142-153. (Pubitemid 34263328)
31. Adamczak, R.; Porollo, A.; Meller, J. Accurate prediction of solvent accessibility using neural networks-based regression. Proteins, 2004, 56(4), 753-767. (Pubitemid 39063318)
32. Lee, B.; Richards, F. M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol., 1971, 55, 379-400.
33. Murzin, A. G.; Brenner, S. E.; Hubbard, T.; Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 1995, 247, 536-540.
34. Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25(17), 3389-3402. (Pubitemid 27359211)
35. Thompson, J.D.; Higgins, D.G.; Gibson T.J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22(22), 4673-4680. (Pubitemid 24354800)
36. Kawashima, S.; Ogata, H.; Kanehisa, M. AAindex: amino acid index database. Nucleic Acids Res. 1999, 27, 368-369. (Pubitemid 29209488)
37. Qi, Y.; Klein-Seetharaman, J.; Bar-Joseph, Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pac. Symp. Biocomput., 2005, 531-542.
38. Díaz-Uriarte, R.; Alvarez de Andrés, S. Gene selection and classification of microarray data using random forest, BMC Bioinformat-ics, 2006, 3.
39. Statnikov, A.; Wang, L.; Aliferis, C.F. A Comprehensive comparison of random forests and support vector machines for microarray-based cancer classification, BMC Bioinformatics, 2008, 9, 319.
40. Ho, T.K. Data complexity analysis of comparative advantages of decision forest constructors, Pattern Analysis and Applications, 2002, 5, 102-112. (Pubitemid 40830124)
41. Breiman, L. Random Forests, Machine Learning, 2001, 45, 5-32. (Pubitemid 32933532)
42. Liaw, A.; Wiener, M. Classification and regression by random forest, R. News, 2002, 2, 18-22.
43. Ho, T.K. The random subspace method for constructing decision forests, IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20, 832-844. (Pubitemid 128741345)