Protein 8-class secondary structure prediction using conditional neural fields

Proteomics

Volumn 11, Issue 19, 2011, Pages 3786-3792

  Wang, Zhiyong ^a   Zhao, Feng ^a   Peng, Jian ^a   Xu, Jinbo ^a  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

Bioinformatics; Conditional neural fields; Eight class; Protein; Secondary structure prediction

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; COMPARATIVE STUDY; CONDITIONAL NEURAL FIELD; PRIORITY JOURNAL; PROBABILITY; PROTEIN 8 CLASS SECONDARY STRUCTURE; PROTEIN ANALYSIS; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; SENSITIVITY ANALYSIS;

ALGORITHMS; DATABASES, PROTEIN; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PROTEIN STRUCTURE, SECONDARY; PROTEINS; PROTEOMICS;

EID: 80053030893     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201100196     Document Type: Article

References (36)

1. Pauling, L., Corey, R. B., Branson, H. R., Two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. USA 1951, 37, 205-211.
2. Kabsch, W., Sander, C., Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22, 2577-2637.
3. Karplus, M., Weaver, D. L., Protein folding dynamics: the diffusion-collision model and experimental data. Protein Sci. 1994, 3, 650-668.
4. Myers, J. K., Oas, T. G., Preorganized secondary structure as an important determinant of fast protein folding. Nat. Struct. Mol. Biol. 2001, 8, 552-558.
5. DeBartolo, J., Colubri, A., Jha, A. K., Fitzgerald, J. E. et al., Mimicking the folding pathway to improve homology-free protein structure prediction. Proc. Natl. Acad. Sci. USA 2009, 106, 3734-3739.
6. Boden, M., Yuan, Z., Bailey, T., Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. BMC Bioinformatics 2006, 7, 68.
7. Cheng, J., Baldi, P., Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 2007, 8, 113.
8. Bodén, M., Bailey, T. L., Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. Bioinformatics 2006, 22, 1809-1814.
9. Pirovano, W., Heringa, J., Protein secondary structure prediction. Data Mining Tech. Life Sci. 2010, 609, 327-348.
10. Qian, N., Sejnowski, T. J., Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 1988, 202, 865-884.
11. Holley, L. H., Karplus, M., Protein secondary structure prediction with a neural network. Proc. Natl. Acad. Sci. USA 1989, 86, 152-156.
12. Kneller, D. G., Cohen, F. E., Langridge, R., Improvements in protein secondary structure prediction by an enhanced neural network. J. Mol. Biol. 1990, 214, 171-182.
13. Rost, B., Sander, C., Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 1993, 232, 584-599.
14. Rost, B., Sander, C., Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 1994, 19, 55-72.
15. Rost, B., PHD: predicting one-dimensional protein structure by profile-based neural networks. Methods Enzymol. 1996, 266, 525-539.
16. Cuff, J. A., Barton, G. J., Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 1999, 34, 508-519.
17. Jones, D. T., Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 1999, 292, 195-202.
18. Rabiner, L. R., A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989, 275-286.
19. Asai, K., Hayamizu, S., Handa, K. I., Prediction of protein secondary structure by the hidden Markov model. Bioinformatics 1993, 9, 141-146.
20. Aydin, Z., Altunbasak, Y., Borodovsky, M., Protein secondary structure prediction for a single-sequence using hidden semi-Markov models. BMC Bioinformatics 2006, 7, 178.
21. 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, 397-407.
22. Kim, H., Park, H., Protein secondary structure prediction based on an improved support vector machines approach. Protein Eng. 2003, 16, 553-560.
23. Ward, J. J., McGuffin, L. J., Buxton, B. F., Jones, D. T., Secondary structure prediction with support vector machines. Bioinformatics 2003, 19, 1650-1655.
24. Guo, J., Chen, H., Sun, Z., Lin, Y., A novel method for protein secondary structure prediction using dual-layer SVM and profiles. Proteins 2004, 54, 738-743.
25. Duan, M., Huang, M., Ma, C., Li, L., Zhou, Y., Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures. Protein Sci. 2008, 17, 1505-1512.
26. 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 2002, 47, 228-235.
27. Peng, J., Bo, L., Xu, J., in: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I., Culotta, A. (Eds.), Advances in Neural Information Processing Systems, MIT Press, vol. 22, 2009, pp. 1419-1427.
28. Zhao, F., Peng, J., DeBartolo, J., Freed, K. et al. in: Batzoglou, S. (Ed.), Research in Computational Molecular Biology, Springer, Berlin/Heidelberg 2009, pp. 59-73.
29. Lafferty, J. D., McCallum, A., Pereira, F. C. N., Proceeding of International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA 2001, pp. 282-289.
30. Zhao, F., Li, S., Sterner, B. W., Xu, J., Discriminative learning for protein conformation sampling. Proteins 2008, 73, 228-240.
31. Liu, D. C., Nocedal, J., On the limited memory BFGS method for large scale optimization. Math. Progr. 1989, 45, 503-528.
32. Meiler, J., Müller, M., Zeidler, A., Schmäschke, F., Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. J. Mol. Model. 2001, 7, 360-369.
33. Tan, Y. H., Huang, H., Kihara, D., Statistical potential-based amino acid similarity matrices for aligning distantly related protein sequences. Proteins 2006, 64, 587-600.
34. Peng, J., Xu, J., Low-homology protein threading. Bioinformatics 2010, 26, 294.
35. Wang, G., Dunbrack, R. L. J., PISCES: a protein sequence culling server. Bioinformatics 2003, 19, 1589-1591.
36. Rost, B., Sander, C., Schneider, R., Redefining the goals of protein secondary structure prediction. J. Mol. Biol. 1994, 235, 13-26.