Prediction of Protein Relative Solvent Accessibility with Support Vector Machines and Long-Range Interaction 3D Local Descriptor

Proteins: Structure, Function and Genetics

Volumn 54, Issue 3, 2004, Pages 557-562

  Kim, Hyunsoo ^a   Park, Haesun ^a  

^a University of Minnesota   (United States)

Author keywords

Directed acyclic graph scheme; Long range interaction; Protein structure prediction; PSSM; Solvent accessibility; Support vector machines

Indexed keywords

AMINO ACID; PROTEIN; SOLVENT;

ACCURACY; ARTICLE; BIOLOGY; CHEMISTRY; COMPUTER SIMULATION; DIRECTED ACYCLIC GRAPH SCHEME; INFORMATION PROCESSING; LONG RANGE INTERACTION THREE DIMENSIONAL LOCAL DESCRIPTOR; METHODOLOGY; MODEL; POSITION SPECIFIC SCORING MATRIX; PREDICTION; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN ENGINEERING; PROTEIN INTERACTION; PROTEIN SOLVENT ACCESSIBILITY; PROTEIN TERTIARY STRUCTURE; PSI BLAST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, PROTEIN; PROTEINS; SENSITIVITY AND SPECIFICITY; SOLVENTS;

EID: 1042268067     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.10602     Document Type: Article

References (50)

1. Baker D, Sali A. Protein structure prediction and structural genomics. Science 2001;294:93-96.
2. Rost B, Sander C. Prediction of secondary structure at better than 70% accuracy. J Mol Biol 1993;232:584-599.
3. King RD, Sternberg MJE. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci 1996;5:2298-2310.
4. Frishman D, Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins 1997;27:329-335.
5. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195-202.
6. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000;40:502-511.
7. Hua SJ, Sun ZR. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 2001;308:397-407.
8. Kim H, Park H. Protein secondary structure prediction based on an improved support vector machines approach. Protein Eng. 2003;16:553-560.
9. Holbrook SR, Muskal SM, Kim SH. Predicting surface exposure of amino acids from protein sequence. Protein Eng 1990;3(8):659-665.
10. Rost B, Sander C. Conservation and prediction of solvent accessibility in protein families. Proteins 1994;20:216-226.
11. Pascarella S, Persio RD, Bossa F, Argos P. Easy method to predict solvent accessibility from multiple protein sequence alignments. Proteins 1999;32:190-199.
12. Li X, Pan XM. New method for accurate prediction of solvent accessibility from protein sequence. Proteins 2001;42:1-5.
13. Pollastri G, Baldi P, Fariselli P, Casadio R. Prediction of coordination number and relative solvent accessibility in proteins. Proteins 2002;47:142-153.
14. Thompson MJ, Goldstein RA. Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins 1996;25:38-47.
15. Naderi-Manesh H, Sadeghi M, Arab S, Movahedi AAM. Predicting of protein surface accessibility with information theory. Proteins 2001;42:452-459.
16. Heiler M. Optimization criteria and learning algorithms for large margin classifiers. Thesis, University of Mannheim, Germany; 2002.
17. Kabsch W, Sander C. A dictionary of protein secondary structure. Biopolymers 1983;22:2577-2637.
18. Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 1988; 202:865-884.
19. Fiser A, Simon I. Predicting the oxidation state of cysteins by multiple sequence alignment. Bioinformatics 2000;3:251-256.
20. Dosztányi Z, Fiser A, Simon I. Stabilization centers in proteins: identification, characterization and predictions. J Mol Biol 1997; 272:597-612.
21. Rose GD, Geselowitz AR, Lesser GJ, Lee RH, Zehfus MH. Hydrophobicity of amino acid residues in globular proteins. Science 1985;229:834-838.
22. 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: 3389-3402.
23. Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 2000;28:45-48.
24. Jones DT, Taylor WR, Thornton JM. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 1994;33:3038-3049.
25. Jones DT, Swindells MB. Getting the most from PSI-BLAST. Trends Biochem Sci 2002;27:161-164.
26. Ahmad S, Gromiha MM. NETASA: neural network based prediction of solvent accessibility. Bioinformatics 2002;18:819-824.
27. Burges CJC, Schölkopf B. Improving the accuracy and speed of support vector learning machines In: Mozer M, Jordan M, Petsche T, editors. Advances in neural information processing systems. Cambridge, MA: MIT Press; 1997. p 375-381.
28. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 1998;2:121-167.
29. Cristianini N, Shawe-Taylor J. Support vecor machines and other kernel-based learning methods. Cambridge University Press, Cambridge; 2000.
30. Osuna E, Freund R, Girosi F. Support vector machines: training and applications. Tech. Rep. AI Memo Now 1602. Cambridge, MA: MIT AI Laboratory; 1997.
31. Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag; 1995.
32. Vapnik V. Statistical learning theory. New York: Wiley; 1998.
33. Hua SJ, Sun ZR. Support vector machine approach for protein subcellular localization preduction. Bioinformatics 2001;17:721-728.
34. Cai YD, Liu XJ, Xu XB, Zhou GP. Support vector machines for predicting protein structural class. BMC Bioinformatics 2001; 2(3).
35. Ding CHQ, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001;17:349-358.
36. Leslie C, Eskin E, Noble WS. The spectrum kernel: a string kernel for SVM protein classification. In: Proceedings of the Pacific Symposium on Biocomputing, 2002. p 564-575.
37. Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000;97:262-267.
38. Mukherjee S, Tamayo P, Mesirov JP, Slonim D, Verri A, Poggio T. Support vector machine classification of microarray data. Tech. Rep. AI Memo No. 1677. Cambridge, MA: MIT; 1999.
39. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Hauessler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000;16:906-914.
40. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002;46: 389-422.
41. Bock JR, Gough DA. Predicting protein-protein interactions from primary structure. Bioinformatics 2001;17:455-460.
42. LeCun Y, Jackel LD, Bottou L, Brunot A, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P, Vapnik V. Comparison of learning algorithms for handwritten digit recognition In: Fogelman F, Gallinari P, editors. International Conference on Artificial Neural Networks, 1995. p 53-60.
43. Blanz V, Scholkopf B, Bulthoff H, Burges C, Vapnik VN, Vetter T. Comparison of view-based object recognition algorithms using realistic 3D models In: von der Malsburg C, von Seelen W, Vorbruggen JC, Sendhoff B, editors. Proceedings of International Conference on Artificial Neural Networks (ICANN 96), 1996. p 251-256.
44. Pontil M, Verri A. Support vector machines for 3D object recognition. IEEE Trans Pattern Anal 1998;20:637-646.
45. Schmidt M. Identifying speakers with support vector networks. In: Proceedings of Interface 96, Sydney, Australia, 1996.
46. Osuna E, Freund R, Girosi F. Training support vector machines: an application to face detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997. p 130-136.
47. Joachims T. Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European Conference on Machine Learning. Berlin: Springer; 1998. p 137-142.
48. Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C. Text classification using string kernels. J Mach Learn Res 2002;2: 419-444.
49. Kim H, Howland P, Park H. Dimension reduction in text classification using support vector machines. J of Mach Learn Res 2003. Forthcoming.
50. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000;28:235-242.