Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers

Biochemical and Biophysical Research Communications

Volumn 384, Issue 2, 2009, Pages 155-159

  Tang, Ke ^a   Pugalenthi, Ganesan ^b   Suganthan, P N ^b   Lanczycki, Christopher J ^c   Chakrabarti, Saikat ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Functionally important sites; Machine learning algorithms; Protein functional templates; Sparse kernel least squares classifiers

Indexed keywords

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BINDING SITE; COMPARATIVE STUDY; ENZYME ACTIVE SITE; LIGAND BINDING; METAL BINDING; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN DATABASE; PROTEIN FAMILY; PROTEIN FUNCTION; PROTEIN STRUCTURE; SENSITIVITY AND SPECIFICITY; SEQUENCE ALIGNMENT;

AMINO ACID SEQUENCE; BINDING SITES; CATALYTIC DOMAIN; LEAST-SQUARES ANALYSIS; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 65649137001     PISSN: 0006291X     EISSN: 10902104     Source Type: Journal    
DOI: 10.1016/j.bbrc.2009.04.096     Document Type: Article

References (31)

1. Fetrow J.S., and Skolnick J. Method for prediction of protein function from sequence using the sequence-to-structure-to-function paradigm with application to glutaredoxins/thioredoxins and T1 ribonucleases. J. Mol. Biol. 281 (1998) 949-968
2. Zhang B., Rychlewski L., Pawlowski K., Fetrow J.S., Skolnick J., and Godzik A. From fold predictions to function predictions: automation of functional site conservation analysis for functional genome predictions. Protein Sci. 8 (1999) 1104-1115
3. Hofmann K., Bucher P., Falquet L., and Bairoch A. The PROSITE database, its status in 1999. Nucleic Acids Res. 27 (1999) 215-219
4. Casari G., Sander C., and Valencia A. A method to predict functional residues in proteins. Nat. Struct. Biol. 2 (1995) 171-178
5. Hannenhalli S.S., and Russell R.B. Analysis and prediction of functional sub-types from protein sequence alignments. J. Mol. Biol. 303 (2000) 61-76
6. Pei J., and Grishin N.V. AL2CO: calculation of positional conservation in a protein sequence alignment. Bioinformatics 17 (2001) 700-712
7. Berezin C., Glaser F., Rosenberg J., Paz I., Pupko T., Fariselli P., Casadio R., and Ben-Tal N N. ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics 20 (2004) 1322-1324
8. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.P., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., Gerald M., Rubin G.M., and Sherlock G. Gene Ontology: tool for the unification of biology. Nat. Genet. 25 (2000) 25-29
9. Bateman A., Birney E., Cerruti L., Durbin R., Etwiller L., Eddy S.R., Griffiths-Jones S., Howe K.L., Marshall M., and Sonnhammer E.L. The Pfam protein families database. Nucleic Acids Res. 30 (2002) 276-280
10. Marchler-Bauer A., Panchenko A.R., Shoemaker B.A., Thiessen P.A., Geer L.Y., and Bryant S.H. CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Res. 30 (2002) 281-283
11. Chakrabarti S., and Lanczycki C.J. Analysis and prediction of FIS in proteins. Protein Sci. 16 (2006) 4-13
12. Lanczycki C.J., and Chakrabarti S. A tool for the prediction of FIS in proteins using a library of functional templates. Bioinformation 2 (2008) 279
13. Rumelhart D.E., and McClelland J.L. Parallel Distributed Processing: Exploration in the Cognition (1986), MIT Press, Cambridge, MA
14. Cortes C., and Vapnik V. Support vector networks. Mach. Learn. 20 (1995) 273-297
15. Thomson R., Hodgman T.C., Yang Z.R., and Doyle A.K. Characterising proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics 19 (2003) 1741-1747
16. Nielsen M., Lundegaard C., Worning P., Lauemoller S.L., Lamberth K., Buss S S., et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12 (2003) 1007-1017
17. Gutteridge A., Bartlett G.J., and Thornton J.M. Using a neural network and spatial clustering to predict the location of active sites in enzymes. J. Mol. Biol. 330 (2003) 719-734
18. Noble W.S. Support vector machine applications in computational biology. In: Schoelkopf B., Tsuda K., and Vert J.-P. (Eds). Kernel Methods in Computational Biology (2004), MIT Press, Cambridge, MA 71-92
19. Ben-Hur A., and Brutlag D. Remote homology detection: a motif based approach. Bioinformatics 19 (2003) 26-33
20. Tang E.K., Suganthan P.N., and Yao X. Gene selection algorithms for microarray data based on least square support vector machine. BMC Bioinformatics 7 (2006) 95
21. Bhardwaj N., and Lu H. Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions. FEBS Lett. 581 (2007) 1058-1066
22. Pugalenthi G., Kumar K.K., Suganthan P.N., and Gangal R. Identification of catalytic residues from protein structure using support vector machine with sequence and structural features. Biochem. Biophys. Res. Commun. 367 (2008) 630-634
23. Pugalenthi G., Tang K., Suganthan P.N., Archunan G., and Sowdhamini R. A machine learning approach for the identification of odorant binding proteins from sequence-derived properties. BMC Bioinformatics 8 (2007) 351
24. McGuffin L.J., Bryson K., and Jones D.T. The PSIPRED protein structure prediction server. Bioinformatics 16 (2000) 404-405
25. Kawashima S., Ogata H., and Kanehisa M. AAindex: amino acid index database. Nucleic Acids Res. 27 (1999) 368-369
26. Engel Y., Mannor S., and Meir R. The kernel recursive least squares algorithm. IEEE Trans. Signal Process. 52 (2004) 2275-2285
27. Rifkin R., Yeo G., and Poggio T. Regularized least squares classification. In: Suykens J.A.K., Horvath G., Basu S., Micchelli C.A., and Vandewalle J. (Eds). Advances in Learning Theory: Methods Models and Applications (2003), IOS Press, Amsterdam 131-154
28. Keerthi S.S., Chapelle O., and DeCoste D. Building support vector machines with reduced classifier complexity. J. Mach. Learn. Res. 8 (2006) 1-22
29. Ding C., and Peng H. Minimum redundancy feature selection for gene expression data. Proc. IEEE Comput. Soc. Bioinform. Conf. (2003) 523-529
30. Cheng G., Qian B., Samudrala R., and Baker D. Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design. Nucleic Acids Res. 33 (2005) 5861-5867
31. Valdar W.S.J. Scoring residue conservation. Proteins Struct. Funct. Genet. 43 (2002) 227-241