A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

Science in China, Series C: Life Sciences

Volumn 48, Issue 4, 2005, Pages 394-405

  Dong, Qiwen ^a   Wang, Xiaolong ^a   Lin, Lei ^a   Guan, Yi ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Maximum entropy Markov model; Protein secondary structure prediction; Protein secondary structure seqlets; Word lattice

Indexed keywords

BACTERIAL PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ANIMAL; ARTICLE; BIOLOGY; CATTLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; ENTROPY; GENETICS; HUMAN; MOLECULAR GENETICS; MOUSE; PREDICTION AND FORECASTING; PROBABILITY; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; AMINO ACID SEQUENCE; ANIMALS; BACTERIAL PROTEINS; CATTLE; COMPUTATIONAL BIOLOGY; ENTROPY; HUMANS; MARKOV CHAINS; MICE; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; PREDICTIVE VALUE OF TESTS; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; SACCHAROMYCES CEREVISIAE;

EID: 28444469641     PISSN: 10069305     EISSN: None     Source Type: Journal    
DOI: 10.1360/062004-53     Document Type: Article

References (41)

1. Thorton, J. M., From genome to function, Science, 2001, 292: 2095-2097.
2. Cheng, L. P., Chen, S. X., Jenifer, M. B. et al. Three-dimensional structure determination of capsid of Aedes albopictus C6/36 cell densovirus, Science in China, Ser. C, 2004, 47(3): 224-228.
3. Liu, Z. Z., Wang, J. L., Wang, Q. et al., Structure expression pattern and chromosomal localization of the rice Osgrp-2 gene, Science in China, Ser. C, 2003, 46(6): 584-594.
4. Chou, P., Fasman, G., Empirical predictions of protein conformation, Annu. Rev. Biochem., 1978, 47(1): 251-276.
5. Ptisyn, O. B., Finkelstein, A. V., Theory of protein secondary structure and algorithm of its prediction, Biopolymers, 1983, 22(1): 15-22.
6. Solovyev, V. V., Salamov, A. A., Method of calculation of discrete secondary structures in globular proteins, J. Mol. Biol., 1991, 25(3): 810-824.
7. Rost, B., Sander, C., Prediction of protein secondary structure at better than 70% accuracy, J. Mol. Biol., 1993, 232(2): 584-599.
8. Hua S., Sun Z., A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach, J. Mol. Biol., 2001, 308(2): 397-407.
9. Rost, B., Sander, C., Combining evolutionary information and neural networks to predict protein secondary structure, Proteins: Struc. Funct. Genet., 1994, 19(1): 55-72.
10. Salzberg, S., Cost, S., Predicting protein secondary structure with nearest-neighbor algorithm, J. Mol. Biol., 1992, 22(2): 7371-7374.
11. Frishman, D., Argos, P., Seventy-five percent accuracy in protein secondary structure prediction, Proteins: Struct. Funct. Genet., 1997, 27(3): 329-335.
12. Salamov, A. A., Solovyev, V. V., Protein secondary structure prediction using local alignments, J. Mol. Biol., 1997, 268(1): 31-36.
13. Schmidler, S. C., Liu, J. S., Brutlag, D. L., Bayesian protein structure prediction, Case Studies in Bayesian Statistics, 2001, 5: 363-378.
14. Schmidler, S. C., Liu, J. S., Brutlag, D. L., Bayesian segmentation of protein secondary structure, J. Comp. Biol., 2000, 7(1/2): 233-248.
15. Language Modeling of Biological Data Workshop, ed. Searles, D., University of Pennsylvania, http://www.ircs.upenn.edu/modeling 2001/modeling.shtml, 2001.
16. Rigoutsos, I., Floratos, A., Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm, Bioinformatics, 1998, 14(1): 55-67.
17. Pisanti, N., Crochemore, M., Grossi, R., Sagot, M. F., A Basis for Repeated Motifs in Pattern Discovery and Text Mining, Institut Gaspard Monge, University of Marne-la-Vallée, IGM 2002-10, Juillet 2002.
18. Rigoutsos, I., Huynh, T., Floratos, A., Panda, L., Platt, D., Dictionary-driven protein annotation, Nucleic Acids Research, 2002, 30(17): 3901-3916.
19. Ganpathiraju, M., Weisser, D., Rosenfeld, R. et al., Comparative n-gram analysis of whole-genome protein sequences, in Proceedings of the Human Language Technologies Conference, San Diego, 2002.
20. McCallum, A., Freitag, D., Pereira, F., Maximum Entropy Markov Models for information extraction and segmentation, in Proceedings of the Seventeenth International Conf. on Machine Learning, Stanford, CA, 2002, 591-598.
21. Rabiner, L. R., Juang, B. H., An introduction to hidden markov models, IEEE ASSP Magazine, 1986, 3(1): 4-16.
22. Berger, A. L., Della Pietra, S. A., Delia Pietra, V. J., A maximum entropy approach to natural language processing, Computational Linguistics, 1996, 22(1): 39-71.
23. Darroch, J. N., Ratcliff, D., Generalized iterative scaling for log-linear models, The Annals of Mathematical Statistics, 1972, 43(5): 1470-1480.
24. Kabsch, W., Sander, C., Dictionary of protein secondary structure: Pattern recognition of hydrogen bonded and geometrical features, Biopolymers, 1983, 22(12): 235-242.
25. Frishman, D., Argos, P., Knowledge-based secondary structure assignment. Proteins: Struc. Funct. Genet., 1995, 23(4): 566-579.
26. Richards, F. M., Kundrot, C. E., Identification of structural motifs from protein coordinate data: Secondary structure and first-level super-secondary structure, Proteins: Struc. Funct. Genet., 1988, 3(2): 71-84.
27. James, A. C., Geoffrey, J. B., Evaluation and improvement of multiple sequence methods for protein secondary structure prediction, Proteins: Struc. Funct. Genet., 1999, 34(4): 508-519.
28. Zemla, A. Venclovas, C., Fidelis, K., Rost, B. A., A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins: Struc. Funct. Genet., 1999, 34(2): 220-223.
29. Rost, B., Sander, C., Schneider, R., Redefining the goals of protein secondary structure prediction, J. Mol. Biol., 1994, 235(1): 13-26.
30. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, L. N., Bourne, P. E., The protein data bank, Nucleic Acids Research, 2000, 28(1): 235-242.
31. Wang, G., Dunbrack, R. L. Jr., PISCES: A protein sequence culling server, Bioinformatics, 2003, 19: 1589-1591.
32. Altschul, S. F., Gish, W., Miller, W., Myers, E. W., Lipman, D. J., Basic local alignment search tool, J. Mol. Biol., 1990, 215: 403-410.
33. Jones, D. T., Protein secondary structure prediction based on position-specific scoring matrices, J. Mol. Biol., 1999, 292: 195-202.
34. Karplus, K., Karchin, R., Barrett, C. et al., What is the value added by human intervention in protein structure prediction? Proteins: Struc. Funct. Genet. 2001, (Suppl. 5): 86-91.
35. Pollastri, G., Przybylski, D., Rost, B., Baldi, P., Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles, Proteins: Struc. Funct., 2002, 47: 228-235.
36. Yan, H. L., Song, Y. L., Liu, F. et al., Homology modeling three-dimensional structure of AnxB1 and reducing its immunogenicity by sequence-deleted mutagenesis, Science in China, Ser. C, 2004, 47(4): 359-367.
37. Cohen, F. E., Abarbanel, R. M., Kuntz, I. D. et al., Turn prediction in proteins using a pattern matching approach, Biochemistry, 1986, 25(1): 266-275.
38. Presnell, S. R., Cohen, B. I., Cohen, F. E., A segment-based approach to protein secondary structure prediction, Biochemistry, 1992, 31(4): 983-993.
39. Crooks, G. E., Brenner, S. E., Protein secondary structure: Entropy, correlations and prediction, Bioinformatics, 2004, 20(10): 1603-1611.
40. Zhou, P., Xie, M. Y., Nie, S. P. et al., Primary structure and configuration of tea polysaccharide, Science in China, Ser. C, 2004, 47(5): 416-424.
41. Rader, A. J., Anderson, G., Isin, B. et al., Identification of core amino acids stabilizing rhodopsin, Proc. Natl. Acad. Sci. USA, 2004, 101(19): 7246-725