A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation

Bioinformatics

Volumn 25, Issue 20, 2009, Pages 2655-2662

  Dong, Qiwen ^a   Zhou, Shuigeng ^a   Guan, Jihong ^b  

^a FUDAN UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; COVARIANCE; INTERMETHOD COMPARISON; MOLECULAR EVOLUTION; MOLECULAR RECOGNITION; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN STRUCTURE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; STRUCTURAL BIOINFORMATICS; SUPPORT VECTOR MACHINE; TAXONOMY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; PATTERN RECOGNITION, AUTOMATED; PROTEIN FOLDING; PROTEINS;

EID: 70349985248     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp500     Document Type: Article

References (35)

1. Altschul,S. et al. (1997) Gapped blast and PSI-blast: A new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402.
2. Andreeva,A. et al. (2004) SCOP database in 2004: Refinements integrate structure and sequence family data. Nucleic Acids Res., 32, D226-D229.
3. Bindewald,E. et al. (2003) Manifold: Protein fold recognition based on secondary structure, sequence similarity and enzyme classification. Protein Eng., 16, 785-789.
4. Chang,C. and Lin,C.J. (2009) LIBSVM: A library for support vector machines.Available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
5. Chen,K. and Kurgan,L. (2007) PFRES: Protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics, 23, 2843-2850.
6. Cheng,J. and Baldi,P. (2006) A machine learning information retrieval approach to protein fold recognition. Bioinformatics, 22, 1456-1463.
7. Damoulas,T. and Girolami,M. (2008) Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection. Bioinformatics, 24, 1264-1270.
8. Deschavanne,P. and Tufféry,P. (2009) Enhanced protein fold recognition using a structural alphabet. Proteins Struct. Funct. Bioinform., 76, 129-137.
9. Ding,C.H. and Dubchak,I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358.
10. Gough,J. et al. (2001) Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J. Mol. Biol., 313, 903-919.
11. Guo,X. and Gao,X. (2008) A novel hierarchical ensemble classifier for protein fold recognition. Protein Eng. Des. Sel., 21, 659-664.
12. Guo,Y. et al. (2006) Predicting g-protein coupled receptors-g-protein coupling specificity based on autocross-covariance transform. Proteins, 65, 55-60.
13. Guo,Y. et al. (2008) Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res., 36, 3025-3030.
14. Kouranov,A. et al. (2006) The RCSB PDB information portal for structural genomics. Nucleic Acids Res., 34, D302-D305.
15. Lapinsh,M. et al. (2002) Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences. Protein Sci., 11 795-805.
16. Le,E. et al. (2005) Multi-class protein fold recognition using adaptive codes. In Proceedings of the 22nd International Conference on Machine learning, ACM New York, NY, USA, pp. 329-336.
17. Lindahl,E. and Elofsson,A. (2000) Identification of related proteins on family, superfamily and fold level. J. Mol. Biol., 295, 613-625.
18. Melvin,I. et al. (2008) Combining classifiers for improved classification of proteins from sequence or structure. BMC Bioinformatics, 9, 389.
19. Moult,J. et al. (2007) Critical assessment of methods of protein structure prediction-round VII. Proteins, 69 (Suppl. 8), 3-9.
20. Rangwala,H. and Karypis,G. (2005) Profile-based direct kernels for remote homology detection and fold recognition. Bioinformatics, 21, 4239-4247.
21. Rangwala,H. and Karypis,G. (2006) Building multiclass classifiers for remote homology detection and fold recognition. BMC Bioinformatics 7, 455.
22. Saigo,H. et al. (2004) Protein homology detection using string alignment kernels. Bioinformatics, 20, 1682-1689.
23. Shamim,M.T. et al. (2007) Support vector machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs. Bioinformatics, 23 3320-3327.
24. Shen,H.B. and Chou,K.C. (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22, 1717-1722.
25. Smith,T.F. and Waterman,M.S. (1981) Identification of common molecular subsequences. J. Mol. Biol., 147, 195-197.
26. Soding,J. et al. (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res., 33, W244-W248.
27. Vapnik,V. (1998) Statistical Learning Theory. The MIT press, New York.
28. Wang,G. and Dunbrack, R. L., Jr. (2004) Scoring profile-to-profile sequence alignments. Protein Sci., 13, 1612-1626.
29. Wold,S. et al. (1993) DNAand peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal. Chim. Acta, 277, 239-253.
30. Wolf,Y.I. et al. (2000) Estimating the number of protein folds and families from complete genome data. J. Mol. Biol., 299, 897-905.
31. Wu,C.H. et al. (2006) The universal protein resource (UniProt): An expanding universe of protein information. Nucleic Acids Res., 34, D187-D191.
32. Wu,S. and Zhang,Y. (2007) LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Res, 35, 3375-3382.
33. Xu,J. (2005) Fold recognition by predicted alignment accuracy. IEEE/ ACM Trans. Comput. Biol. Bioinform., 2, 157-165.
34. Xu,J. et al. (2003) RAPTOR: Optimal protein threading by linear programming. J. Bioinform. Comput. Biol., 1, 95-117.
35. Zhang,W. et al. (2008) SP5: Improving protein fold recognition by using torsion angle profiles and profile-based gap penalty model. PLoS ONE, 3, e2325.