Protease substrate site predictors derived from machine learning on multilevel substrate phage display data

Bioinformatics

Volumn 24, Issue 23, 2008, Pages 2691-2697

  Chen, Ching Tai ^a,b   Yang, Ei Wen ^a   Hsu, Hung Ju ^c,d   Sun, Yi Kun ^c   Hsu, Wen Lian ^a   Yang, An Suei ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^c GENOMICS RESEARCH CENTER   (Taiwan)

^d NATIONAL DEFENSE UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD CLOTTING FACTOR 10A; PROTEINASE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PREDICTION; CROSS VALIDATION TEST; ENZYME SPECIFICITY; ENZYME SUBSTRATE; INFORMATION PROCESSING; LEARNING ALGORITHM; MACHINE LEARNING; METHODOLOGY; PHAGE DISPLAY; PRIORITY JOURNAL; PROTEOMICS; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 56649119163     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn538     Document Type: Article

References (38)

1. Backes,C. et al. (2005) GraBCas: A bioinformatics tool for score-based prediction of Caspase- and Granzyme B-cleavage sites in protein sequences. Nucleic Acids Res., 33, W208-W213.
2. Boyd,S.E. et al. (2004) PoPS: A computational tool for modeling and predicting protease specificity. In Proceedings of the IEEE Computational Systems Bioinformatics Conference, pp. 372-381, Stanford, CA.
3. Brandstetter,H. et al. (1996) X-ray structure of active site-inhibited clotting factor Xa. Implications for drug design and substrate recognition. J. Biol. Chem., 271, 29988-29992.
4. Breiman,L. (1996) Bagging predictors. Mach. Learn., 24, 123-140.
5. Burges,C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov., 2, 121-167.
6. Chang,C.C. and Lin,C.J. (2001) LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm. (last accessed date February 27, 2007).
7. Coombs,G.S. et al. (1999) Revisiting catalysis by chymotrypsin family serine proteases using peptide substrates and inhibitors with unnatural main chains. J. Biol. Chem., 274, 24074-24079.
8. Deperthes,D. (2002) Phage display substrate: A blind method for determining protease specificity. Biol. Chem., 383, 1107-1112.
9. Ding,X. et al. (2006) Direct crystallographic observation of an acyl-enzyme intermediate in the elastase-catalyzed hydrolysis of a peptidyl ester substrate: Exploiting the "glass transition" in protein dynamics. Bioorg. Chem., 34, 410-423.
10. Garay-Malpartida,H.M. et al. (2005) CaSPredictor: A new computer-based tool for caspase substrate prediction. Bioinformatics 21 (Suppl. 1), i169-i176.
11. Gosalia,D.N. et al. (2005) Profiling serine protease substrate specificity with solution phase fluorogenic peptide microarrays. Proteomics, 5, 1292-1298.
12. Guertin,K.R. and Choi,Y.M. (2007) The discovery of the Factor Xa inhibitor otamixaban: From lead identification to clinical development. Curr. Med. Chem., 14, 2471-2481.
13. Harris,J.L. et al. (2000) Rapid and general profiling of protease specificity by using combinatorial fluorogenic substrate libraries. Proc. Natl Acad. Sci. USA, 97, 7754-7759.
14. Hedstrom,L. (2002) Serine protease mechanism and specificity. Chem. Rev., 102, 4501-4524.
15. Hertzberg,M. (1994) Biochemistry of factor X. Blood Rev., 8, 56-62.
16. Hsu,H.J. et al. (2008) Factor Xa active site substrate specificity with substrate phage display and computational molecular modeling. J. Biol. Chem., 283, 12343-12353.
17. Jenny,R.J. et al. (2003) A critical review of the methods for cleavage of fusion proteins with thrombin and factor Xa. Protein Expr. Purif., 31, 1-11.
18. Jin,Z. and El-Deiry,W.S. (2005) Overview of cell death signaling pathways. Cancer Biol. Ther., 4, 139-163.
19. Kawashima,S. and Kanehisa,M. (2000) AAindex: Amino acid index database. Nucleic Acids Res., 28, 374.
20. Laskowski,M. and Qasim,M.A. (2000) What can the structures of enzyme-inhibitor complexes tell us about the structures of enzyme substrate complexes? Biochim. Biophys. Acta, 1477, 324-337.
21. Lin,H.N. et al. (2005) HYPROSP II - a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence. Bioinformatics, 21, 3227-3233.
22. Liu,W. et al. (2006) Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics, 7, 182.
23. Manning,C.D. et al. (2007) An Introduction to Information Retrieval. Cambridge University Press, Cambridge, England.
24. Marnett,A.B. and Craik,C.S. (2005) Papa's got a brand new tag: Advances in identification of proteases and their substrates. Trends Biotechnol., 23, 59-64.
25. Matthews,B.W. (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 405, 442-451.
26. Matthews,D.J. and Wells,J.A. (1993) Substrate phage: Selection of protease substrates by monovalent phage display. Science, 260 1113-1117.
27. Narayanan,A. et al. (2002) Mining viral protease data to extract cleavage knowledge. Bioinformatics, 18 (Suppl. 1), S5-S13.
28. Ohkubo,S. et al. (2001) Substrate phage as a tool to identify novel substrate sequences of proteases. Comb. Chem. High Throughput Screen., 4, 573-583.
29. Packard,B.Z. and Komoriya,A. (2008) Intracellular protease activation in apoptosis and cell-mediated cytotoxicity characterized by cell-permeable fluorogenic protease substrates. Cell Res., 18, 238-247.
30. Pissarnitski,D. (2007) Advances in gamma-secretase modulation. Curr. Opin. Drug Discov. Devel., 10, 392-402.
31. Rawlings,N.D. et al. (2008) MEROPS: The peptidase database. Nucleic Acids Res., 36, D320-D325.
32. Rumelhart,D.E. et al. (1986) Learning Internal Representations by Error Propagation. MIT Press, Cambridge, MA, USA.
33. Salisbury,C.M. et al. (2002) Peptide microarrays for the determination of protease substrate specificity. J. Am. Chem. Soc. 124, 14868-14870.
34. Sharkov,N.A. et al. (2001) Reaction kinetics of protease with substrate phage. Kinetic model developed using stromelysin. J. Biol. Chem., 276, 10788-10793.
35. Smith,M.M. et al. (1995) Rapid identification of highly active and selective substrates for stromelysin and matrilysin using bacteriophage peptide display libraries. J. Biol. Chem., 270, 6440-6449.
36. Tyndall,J.D. et al. (2005) Proteases universally recognize beta strands in their active sites. Chem. Rev., 105, 973-999.
37. Wu,T.F. et al. (2004) Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res., 5 975-1005.
38. Yang,Z.R. (2005) Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: A novel method for decisive template selection. Bioinformatics, 21, 2644-2650.