PFRES: Protein fold classification by using evolutionary information and predicted secondary structure

Bioinformatics

Volumn 23, Issue 21, 2007, Pages 2843-2850

  Chen, Ke ^a   Kurgan, Lukasz ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; AUTOMATION; CLASSIFICATION ALGORITHM; COMPUTER PROGRAM; EVOLUTIONARY ALGORITHM; PREDICTION; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; SEQUENCE HOMOLOGY; STRUCTURAL HOMOLOGY; STRUCTURAL PROTEOMICS;

EID: 36448960843     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm475     Document Type: Article

References (44)

1. Aha,D. and Kibler,D. (1991) Instance-based learning algorithms. Mach. Learn., 6, 37-66.
2. Altschul,S.F. et al. (1997) Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 17, 3389-3402.
3. Andreeva,A. et al. (2004) SCOP database in 2004: Refinements integrate structure and sequence family data. Nucleic Acids Res., 32, D226-D229.
4. Birzele,F. and Kramer,S. (2006) A new representation for protein secondary structure prediction based on frequent patterns. Bioinformatics, 22, 2628-2634.
5. Bologna,G. and Appel,R.D. (2002) A comparison study on protein fold recognition. In Proceedings of the 9th International Conference on Neural Information Processing. Vol. 5, pp. 2492-2496.
6. Bujnicki,J.M. (2006) Protein structure prediction by recombination of fragments. Chem. BioChem., 7, 19-27.
7. Chandonia,J.M. and Brenner,S.E. (2006) The impact of structural genomics: Expectations and outcomes. Science, 311, 347-351.
8. Chen,K. et al. (2007) Prediction of flexible/rigid region from protein sequences using k-spaced amino acid pairs. BMC Struct. Biol. 7, 25.
9. Chou,K.C. (2005) Progress in protein structural class prediction and its impact to bioinformatics and proteomics. Curr. Protein Pept. Sci., 6, 423-436.
10. Chothia,C. (1992) Proteins. One thousand families for the molecular biologist. Nature, 357, 543-544.
11. Cleary,J.G. and Trigg,L.E. (1995) K*: An instance-based learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine Learning, pp. 108-114.
12. Ding,C.H. and Dubchak,I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358.
13. Eyrich,V.A. et al. (2001) EVA: Continuous automatic evaluation of protein structure prediction servers. Bioinformatics, 17, 1242-1243.
14. Holley,L.H. and Karplus,M. (1989) Protein secondary structure prediction with a neural network. Proc. Natl Acad. Sci. USA, 86, 152-156.
15. John,G.H. and Langley,P. (1995) Estimating continuous distributions in Bayesian classifiers. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338-345.
16. Joned,D.T. (1999) Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 292, 195-202.
17. Jones,D.T. (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23, 538-544.
18. Kerthi,S.S. et al. (2001) Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput, 13, 637-649.
19. Kim,H. and Park,H. (2004) Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins, 54, 557-562.
20. Kurgan,L and Chen,K. (2007) Prediction of protein structural class for the twilight zone sequences. Biochem. Biophys. Res. Commun., 357, 453-460.
21. Le,C.S. and Houwelingen,J.C. (1992) Ridge estimators in logistic regression. Appl. Slat., 41, 191-201.
22. Leo,B. (2001) Random forests. Mach. Learn., 1, 5-32.
23. Levitt,M. (2007) Growth of novel protein structural data. Proc. Natl Acad. Sci. USA, 104, 3183-3188.
24. Levitt,M. and Chothia,C. (1976) Structural patterns in globular proteins. Nature, 261, 552-558.
25. Li,W. and Godzik,A. (2006) Cd-hit a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22, 1658-1659.
26. Lin,K. et al. (2005) A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics, 21, 152-159.
27. Murzin,A.G, et al. (1995) SCOP: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536-5340.
28. Nanni,L. (2006) A novel ensemble of classifiers for protein fold recognition. Neurocomputing., 69, 2434-2437.
29. Okun,O. (2004) Protein fold recognition with K-local hyperplane distance nearest neighbor algorithm In Proceedings of the 2nd European Workshop on Data Mining and Text Mining in Bioinformatics. Vol. 1, pp. 51-57.
30. Paiardini,A. et al. (2004) Evolutionarily conserved regions and hydrophobic contacts at the superfamily level: The case of the fold-type I, pyridoxal-5′-phosphate-dependent enzymes. Protein Sci., 13 2992-3005.
31. Quian,N. and Sejnowski,T.J. (1988) Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol., 202, 865-884.
32. Reinhardt,A. and Eisenberg,D. (2004) DPANN: Improved, sequence to structure alignments following fold recognition Proteins, 56 528-538.
33. Ruan,J. et al. (2006) Quantitative analysis of the conservation of the tertiary structure of protein segments. Protein J., 25, 301-315.
34. Seewald,A.K. (2002) How to make stacking better and faster while also taking care of an unknown weakness. In Proceedings of the 19th International Conference on Machine Learning, pp. 554-561.
35. Seewald,A.K. and Fuernkranz,J (2001) An evaluation of grading classifiers. In Proceedings of 4th International Conference on Advances in Intelligent Data Analysis, pp. 115-124.
36. Shen,H.B. and Chou,K.C. (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22, 1717-1722.
37. Smith,T.F. and Waterman,M.S. (1981) Identification of common molecular subsequences. J. Mol. Biol., 147, 195-197.
38. Tomii,K. et al. (2005) Protein structure prediction using a variety of profile libraries and 3D verification. Proteins, 61 (7), 114-121.
39. Tress,M. et al. (2005) Assessment of predictions submitted for the CASP6 comparative modeling category. Proteins, 61 (7), 27-45.
40. Wang,G. et al. (2005) Assessment of fold recognition predictions in CASP6. Proteins, 61 (Suppl. 7), 46-66.
41. Witten,I. and Frank,E. (2005) Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco.
42. Yu,L. and Liu,H. (2003) Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 10th International Conference on Machine Learning, pp. 856-863.
43. Yu,Y.K. et al. (2006) Retrieval accuracy, statistical significance and compositional similarity in protein sequence database searches. Nucleic Acids Res., 34, 5966-5973.
44. Zhang,Y. and Skolnick,J. (2005) The protein structure prediction problem could be solved using the current PDB library. Proc. Natl Acad. Sci. USA, 102, 1029-1034.