Real value prediction of protein solvent accessibility using enhanced PSSM features

BMC Bioinformatics

Volumn 9, Issue SUPPL. 12, 2008, Pages

  Chang, Darby Tien Hao ^a   Huang, Hsuan Yu ^a   Syu, Yu Tang ^a   Wu, Chih Peng ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ACCESSIBLE SURFACE AREAS; EVOLUTIONARY INFORMATION; PHYSICOCHEMICAL PROPERTY; POSITION SPECIFIC SCORING MATRIX; SELECTION MECHANISM; SOLVENT ACCESSIBILITY; SUPPORT VECTOR REGRESSION (SVR); TERTIARY STRUCTURES; NUMBER OF METHODS; PROTEIN STRUCTURES; REAL VALUES; REGRESSION PROBLEM; SELECTION PROCESS; SYSTEMATIC METHOD;

AMINO ACIDS; PROTEINS; SOLVENTS; BIOINFORMATICS;

FORECASTING;

SOLVENT; AMINO ACID; PROTEIN;

AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER PREDICTION; CONFERENCE PAPER; CONTROLLED STUDY; INTERMETHOD COMPARISON; MULTIPLE LINEAR REGRESSION ANALYSIS; PHYSICAL CHEMISTRY; PROTEIN TERTIARY STRUCTURE; SUPPORT VECTOR MACHINE; ALGORITHM; ARTICLE; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; METHODOLOGY; PROTEIN CONFORMATION; PROTEIN DATABASE; REGRESSION ANALYSIS; REPRODUCIBILITY; SEQUENCE ANALYSIS; STATISTICAL MODEL; SURFACE PROPERTY;

ALGORITHMS; AMINO ACIDS; CHEMISTRY, PHYSICAL; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, STATISTICAL; PROTEIN CONFORMATION; PROTEINS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SOLVENTS; SURFACE PROPERTIES;

EID: 57649201318     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-S12-S12     Document Type: Conference Paper

References (37)

1. Mount DW Bioinformatics: Sequence and genome analysis Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press 2 2004
2. Chan HS Dill KA Origins of Structure in Globular-Proteins Proc Natl Acad Sci USA 1990, 87(16):6388-6392 54539 2385597 10.1073/pnas.87.16.6388
3. Raih MF Ahmad S Zheng R Mohamed R Solvent accessibility in native and isolated domain environments: General features and implications to interface predictability Biophys Chem 2005, 114(1):63-69 10.1016/ j.bpc.2004.10.005 15792862
4. Holbrook SR Muskal SM Kim SH Predicting Surface Exposure of Amino-Acids from Protein-Sequence Protein Eng 1990, 3(8):659-665 10.1093/protein/ 3.8.659 2217139
5. Rost B Sander C Conservation and Prediction of Solvent Accessibility in Protein Families Proteins 1994, 20(3):216-226 10.1002/prot.340200303 7892171
6. Pascarella S De Persio R Bossa F Argos P Easy method to predict solvent accessibility from multiple protein sequence alignments Proteins 1998, 32(2):190-199 10.1002/ (SICI)1097-0134(19980801)32:2<190::AID-PROT5>3.0.CO;2-P 9714158
7. Cuff JA Barton GJ Application of multiple sequence alignment profiles to improve protein secondary structure prediction Proteins 2000, 40(3):502-511 10.1002/ 1097-0134(20000815)40:3<502::AID-PROT170>3.0.CO;2-Q 10861942
8. Fariselli P Casadio R RCNPRED: Prediction of the residue co-ordination numbers in proteins Bioinformatics 2001, 17(2):202-203 10.1093/ bioinformatics/17.2.202 11238082
9. Li X Pan XM New method for accurate prediction of solvent accessibility from protein sequence Proteins 2001, 42(1):1-5 10.1002/ 1097-0134(20010101)42:1<1::AID-PROT10>3.0.CO;2-N 11093255
10. Ahmad S Gromiha MM NETASA: Neural network based prediction of solvent accessibility Bioinformatics 2002, 18(6):819-824 10.1093/bioinformatics/ 18.6.819 12075017
11. Pollastri G Baldi P Fariselli P Casadio R Prediction of coordination number and relative solvent accessibility in proteins Proteins 2002, 47(2):142-153 10.1002/prot.10069 11933061
12. Thompson MJ Goldstein RA Predicting solvent accessibility: Higher accuracy using Bayesian statistics and optimized residue substitution classes Proteins 1996, 25(1):38-47 10.1002/ (SICI)1097-0134(199605)25:1<38::AID-PROT4>3.3.CO;2-H 8727318
13. Mucchielli-Giorgi MH Hazout S Tuffery P PredAcc: Prediction of solvent accessibility Bioinformatics 1999, 15(2):176-177 10.1093/bioinformatics/ 15.2.176 10089205
14. Richardson CJ Barlow DJ The bottom line for prediction of residue solvent accessibility Protein Eng 1999, 12(12):1051-1054 10.1093/protein/ 12.12.1051 10611398
15. Carugo O Predicting residue solvent accessibility from protein sequence by considering the sequence environment Protein Eng 2000, 13(9):607-609 10.1093/protein/13.9.607 11054454
16. Naderi-Manesh H Sadeghi M Arab S Movahedi AAM Prediction of protein surface accessibility with information theory Proteins 2001, 42(4):452-459 10.1002/ 1097-0134(20010301)42:4<452::AID-PROT40>3.0.CO;2-Q 11170200
17. Yuan Z Burrage K Mattick JS Prediction of protein solvent accessibility using support vector machines Proteins 2002, 48(3):566-570 10.1002/ prot.10176 12112679
18. Kim H Park H Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor Proteins 2004, 54(3):557-562 10.1002/prot.10602 14748002
19. Nguyen MN Rajapakse JC Prediction of protein relative solvent accessibility with a two-stage SVM approach Proteins 2005, 59(1):30-37 10.1002/prot.20404 15696542
20. Gianese G Bossa F Pascarella S Improvement in prediction of solvent accessibility by probability profiles Protein Eng 2003, 16(12):987-992 10.1093/protein/gzg139 14983079
21. Altschul SF Madden TL Schaffer AA Zhang JH Zhang Z Miller W Lipman DJ Gapped BLAST and PSI-BLAST: A new generation of protein database search programs Nucleic Acids Res 1997, 25(17):3389-3402 146917 9254694 10.1093/ nar/25.17.3389
22. Ahmad S Gromiha MM Sarai A Real value prediction of solvent accessibility from amino acid sequence Proteins 2003, 50(4):629-635 10.1002/prot.10328 12577269
23. Yuan Z Huang BX Prediction of protein accessible surface areas by support vector regression Proteins 2004, 57(3):558-564 10.1002/ prot.20234 15382233
24. Adamczak R Porollo A Meller J Accurate prediction of solvent accessibility using neural networks-based regression Proteins 2004, 56(4):753-767 10.1002/prot.20176 15281128
25. Wang JY Lee HM Ahmad S Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression Proteins 2005, 61(3):481-491 10.1002/prot.20620 16170780
26. Garg A Kaur H Raghava GPS Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure Proteins 2005, 61(2):318-324 10.1002/prot.20630 16106377
27. Nguyen MN Rajapakse JC Two-stage support vector regression approach for predicting accessible surface areas of amino acids Proteins 2006, 63(3):542-550 10.1002/prot.20883 16456847
28. Shimizu K Hirose S Noguchi T Muraoka Y Predicting the protein disordered region using modified position specific scoring matrix 15th International Conference on Genome Informatics: December 16-18 2004; Yokohama Pacifico, Japan 2004:150
29. Su CT Chen CY Ou YY Protein disorder prediction by condensed PSSM considering propensity for order or disorder BMC Bioinformatics 2006, 7:319 1526762 16796745 10.1186/1471-2105-7-319
30. Kabsch W Sander C Dictionary of Protein Secondary Structure - Pattern-Recognition of Hydrogen-Bonded and Geometrical Features Biopolymers 1983, 22(12):2577-2637 10.1002/bip.360221211 6667333
31. Eisenhaber F Argos P Improved Strategy in Analytic Surface Calculation for Molecular-Systems - Handling of Singularities and Computational-Efficiency Journal of Computational Chemistry 1993, 14(11):1272-1280 10.1002/jcc.540141103
32. Ooi T Oobatake M Nemethy G Scheraga HA Accessible Surface-Areas as a Measure of the Thermodynamic Parameters of Hydration of Peptides Proc Natl Acad Sci USA 1987, 84(10):3086-3090 304812 3472198 10.1073/ pnas.84.10.3086
33. Chang CC Lin CJ LIBSVM: A library for support vector machines 2001 http://www.csie.ntu.edu.tw/~cjlin/libsvm
34. Jones DT Protein secondary structure prediction based on position-specific scoring matrices J Mol Biol 1999, 292(2):195-202 10.1006/jmbi.1999.3091 10493868
35. Jones DT Swindells MB Getting the most from PSI-BLAST Trends Biochem Sci 2002, 27(3):161-164 10.1016/S0968-0004(01)02039-4 11893514
36. Zhang QD Yoon SJ Welsh WJ Improved method for predicting beta-turn using support vector machine Bioinformatics 2005, 21(10):2370-2374 10.1093/ bioinformatics/bti358 15797917
37. Witten IH Frank E Data mining: Practical machine learning tools and techniques Amsterdam; Boston, MA: Morgan Kaufman 2 2005