Exploratory predicting protein folding model with random forest and hybrid features

Current Proteomics

Volumn 11, Issue 4, 2014, Pages 289-299

  Zhao, Xuewei ^a,d   Zou, Quan ^a,c   Liu, Bin ^b,c   Liu, Xiangrong ^a  

^a XIAMEN UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c Shanghai Key Laboratory of Intelligent Information Processing   (China)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Classifier; n gram feature; Protein class; Protein structure prediction; Random forests; SCOP dataset

Indexed keywords

AMINO ACID; CELL SURFACE PROTEIN; PEPTIDE; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; DECISION TREE; HYBRID; HYDROGEN BOND; PREDICTION; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; RANDOM FOREST;

EID: 84922326160     PISSN: 15701646     EISSN: None     Source Type: Journal    
DOI: 10.2174/157016461104150121115154     Document Type: Article

References (46)

1. Chou, K.-C. and Zhang, C.-T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
2. Deleage, G. and Roux, B. An algorithm for protein secondary structure prediction based on class prediction. Protein Eng., 1987, 1(4), 289-294.
3. Cid, H.; Bunster, M.; Canales, M. and Gazitúa, F. Hydrophobicity and structural classes in proteins. Protein Eng., 1992, 5(5), 373-375.
4. Nishikawa, K.; Kubota, Y. and Tatsuo, O. Classification of proteins into groups based on amino acid composition and other characters. II. Grouping into four types. J. Biochem., 1983, 94(3), 997-1007.
5. Nishikawa, K.; Kubota Y. and Tatsuo, O. Classification of proteins into groups based on amino acid composition and other characters. I. Angular distribution. J. Biochem., 1983, 94(3), 981-995.
6. Levitt, M. and Chothia, C. Structural patterns in globular proteins. Nature, 1976, 261(5561), 552-558.
7. Efimov, A. V. Structural similarity between two-layer -/- and β-proteins. J. Mol. Biol., 1995, 245(4), 402-415.
8. Cai, Y.-D.; Liu, X.-J.; Xu, X.-B. and Chou, K. C. Prediction of protein structural classes by support vector machines. Comput. Chem., 2002, 26(3), 293-296.
9. Shen, H.-B. and Chou, K.-C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem., 2008, 373(2), 386-388.
10. Liu, B.; Wang, X.; Zou, Q.; Dong, Q. and Chen, Q. Protein remote homology detection by combining chou's pseudo amino acid composition and profile-based protein representation. Mol. Inform., 2013, 32(9), 775-782.
11. Huang, T.; Chen, L.; Cai, Y. D. and Chou, K. C. Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PloS One, 2011, 6(9), e25297.
12. Lin, C.; Zou, Y.; Qin, J.; Liu, X.; Jiang, Y.; Ke, C. and Zou, Q. Hierarchical classification of protein folds using a novel ensemble classifier. PloS One, 2013, 8(2), e56944.
13. Zou, Q.; Wang, Z.; Guan, X.; Liu, B.; Wu, Y. and Lin, Z. An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier. BioMed Res. Int., 2013, 2013, 686090.
14. Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 1999, 292(2), 195-202.
15. Dong, Q.; Zhou S. and Guan, J. A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics, 2009, 25(20), 2655-2662.
16. Quan, Z.; Weicheng, C.; Yong, H.; Xiangrong, L. and Yi, J. Identifying Multi-Functional Enzyme by Hierarchical Multi-Label Classifier. J. Comput. Theoret. Nanosci., 2013, 10(4), 1038-1043.
17. Zou, Q.; Li, X.; Jiang, Y.; Zhao, Y. and Wang, G. BinMemPredict: a web server and software for predicting membrane protein types. Curr. Proteom., 2013, 10(1), 2-9.
18. Liu, B.; Wang, X.; Lin, L.; Tang, B.; Dong, Q. and Wang X. Prediction of protein binding sites in protein structures using hidden Markov support vector machine. BMC Bioinform., 2009, 10(1), 381.
19. Liu, B.; Wang, X.; Lin, L.; Dong, Q. and Wang, X. Exploiting three kinds of interface propensities to identify protein binding sites. Computat. Biol. Chem., 2009, 33, 303-311.
20. Liu, B.; Wang, X.; Chen, Q. Dong, Q. and Lan, X. Using Amino Acid Physicochemical Distance Transformation for Fast Protein Remote Homology Detection. PloS one, 2012, 7(9), e46633.
21. Lin, C.; Chena, W.; Qiua, C.; Wua, Y.; Krishnanc, S. and Zou, Q. LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing, 2014, 123, 424-435.
22. Murzin, A. G.; Brenner, S. E.; Hubbard, T.; and Chothia, C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 1995, 247(4), 536-540.
23. Andreeva, A.; Howorth, D.; Chandonia, J.-M.; Brenner, S. E.; Hubbard, T. J.; Chothia, C. and Murzin, A. G. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Res., 2008, 36, D419-D425.
24. Chothia, C. and Lesk, A. M. The relation between the divergence of sequence and structure in proteins. EMBO J., 1986, 5(4), 823.
25. Lesk, A. and Chothia, C. The response of protein structures to amino-acid sequence changes. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, vol. 317, no. 1540, pp. 345-356, 1986.
26. Brown, P. F.; Desouza, P. V.; Mercer, R. L. Pietra, V. J. D. and Lai, J. C. Class-based n-gram models of natural language. Comput linguistics, 1992, 18(4), 467-479.
27. Suen, C. Y. N-gram statistics for natural language understanding and text processing. IEEE Trans. Pattern Anal. Mach. Intell., 1979, 2, 164-172.
28. Cavnar, W. B. N-gram-based text filtering for TREC-2. Ann. Arbor., 1993, 1001, 48113-44001.
29. Cavnar, W. B. and Trenkle, J. M. N-gram-based text categorization. Ann. Arbor MI, 1994, 48113, 161-175.
30. Liu, B.; Wang, X.; Lin, L.; Dong, Q. and Wang, X. A Discriminative Method for Protein Remote Homology Detection and Fold Recognition Combining Top-n-grams and Latent Semantic Analysis. BMC Bioinformatics, 2008, 9, 510.
31. Wu, C.; Whitson, G.; McLarty, J.; Ermongkonchai, A. and Chang, T. C. Protein classification artificial neural system. Protein Sci., 1992, 1, 667-677.
32. Guo, F. B.; Huang, J.; Rao, N.; Chen, W. and Lin, H. Prediction of Golgi-resident protein types by using feature selection technique. Chemometrics Intelligent Laborat. Sys., 2013, 124, 9-13.
33. Lin, H.; Chen, W.; Yuan, L. F.; Li, Z. Q. and Ding, H. Using overrepresented tetrapeptides to predict protein submitochondrial locations. Acta Biotheoretica, 2013, 61(2), 259-268.
34. Breiman, L. Random forests. Machine Learn., 2001, 45(1), 5-32.
35. Ho, T. K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, 832-844.
36. Amit, Y. and Geman, D. Shape quantization and recognition with randomized trees. Neural. Comput., 1997, 9, 1545-1588.
37. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P. and Witten, I. H. The WEKA data mining software: an update. ACM SIGKDD Expl. Newsletter. 2009, 11, 10-18.
38. Guo, S. H.; Deng, E. Z.; Xu, L. Q.; Ding, H.; Lin, H.; Chen, W. and Chou, K. C. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics, 2014, 30(11), 1522-1529.
39. Ding, H.; Feng, P. M.; Chen, W. and Lin, H. Identification of bacteriophage virion proteins with the ANOVA feature selection and analysis. Mol. Biosys., 2014, 10, 2229-2235.
40. Lin, H.; Chen, W. and Ding, H. AcalPred: A sequence-based tool for discriminating between acidic and alkaline enzymes. PloS One, 2013, 8(10), e75726.
41. Lin, H.; Ding, C.; Song, Q.; Yang, P.; Ding, H.; Deng, K. J. and Chen, W. The prediction of protein structural class using averaged chemical shifts. J. Biomol. Struct. Dynamics, 2012, 29(6), 643-648.
42. Ding, H.; Lin, H.; Chen, W.; Li, Z. Q.; Guo, F. B.; Huang, J. and Rao, N. Prediction of protein structural classes based on feature selection technique. Interdisciplinary Sci. Comput. Life Sci., 2014, 6(3), 235-240.
43. Liu, B.; Jinghao, X.; Shixi, F.; Ruifeng, X.; Jiyun, Z. and Wang, X. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. Mol. Inform., DOI: 10. 1002/minf. 201400025.
44. Liu, B.; Xu, J.; Lan, X.; Xu, R.; Zhou, J.; Wang, X. and Chou, K.-C. iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition. PLoS ONE, 2014, 9, e106691.
45. Liu, B.; Xu, J.; Zou, Q.; Xu, R.; Wang, X. and Chen, Q. Using distances between Top-n-gram and residue pairs for protein remote homology detection. BMC Bioinformatics, 2014, 15, S3.
46. Liu, B.; Zhang, D.; Xu, R.; Xu, J.; Wang, X.; Chen, Q.; Dong, Q.; and Chou, K.-C. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics, 2014, 30, 472-479.