Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets

Bioinformatics

Volumn 30, Issue 1, 2014, Pages 71-80

  Wang, Mingjun ^a   Zhao, Xing Ming ^b   Tan, Hao ^c   Akutsu, Tatsuya ^d   Whisstock, James C ^c   Song, Jiangning ^a,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b TONGJI UNIVERSITY   (China)

^c MONASH UNIVERSITY   (Australia)

^d KYOTO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CASPASE; GRANZYME; PROTEOME;

AMINO ACID SEQUENCE; CHEMISTRY; ENZYME SPECIFICITY; HUMAN; METABOLISM; PROBABILITY; SOFTWARE DESIGN; ARTICLE; COMPUTER PROGRAM;

AMINO ACID SEQUENCE; CASPASES; GRANZYMES; HUMANS; PROBABILITY; PROTEOME; SOFTWARE DESIGN; SUBSTRATE SPECIFICITY;

EID: 84891350626     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt603     Document Type: Article

References (67)

1. Altschul, S.F. et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 3389-3402.
2. Araya, R. et al. (2002) Yeast two-hybrid screening using constitutive-active caspase-7 as bait in the identification of PA28gamma as an effector caspase substrate. Cell Death Differ., 9, 322-328.
3. Ashburner, M. et al. (2000) Gene ontology: tool for the unification of biology. Nat. Genet., 25, 25-29.
4. Ayyash, M. et al. (2012) Developing a powerful in silico tool for the discovery of novel caspase-3 substrates: a preliminary screening of the human proteome. BMC Bioinformatics, 13, 14.
5. 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, W208-213.
6. Bairoch, A. et al. (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res., 33, D154-D159.
7. Barkan, D.T. et al. (2010) Prediction of protease substrates using sequence and structure features. Bioinformatics, 26, 1714-1722.
8. Bogdanova, N. et al. (2007) A common haplotype of the annexin A5 (ANXA5) gene promoter is associated with recurrent pregnancy loss. Hum. Mol. Genet., 16, 573-578.
9. Boyd, S.E. et al. (2005) PoPS: a computational tool for modeling and predicting protease specificity. J. Bioinform. Comput. Biol., 3, 551-585.
10. Bredemeyer, A.J. et al. (2005) Use of protease proteomics to discover granzyme B substrates. Immunol. Res., 32, 143-153.
11. Burges, C.J.C. (1998) A tutorial on Support Vector Machines for pattern recognition. Data Min. Knowl. Discov., 2, 121-167.
12. Chang, C.-C. and Lin, C.-J. (2011) LIBSVM:a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 1-26.
13. Chen, C.T. et al. (2008) Protease substrate site predictors derived from machine learning on multilevel substrate phage display data. Bioinformatics, 24, 2691-2697.
14. Chowdhury, I. et al. (2008) Caspases-an update. Comp. Biochem. Physiol. B Biochem. Mol. Biol., 151, 10-27.
15. Colaert, N. et al. (2009) Improved visualization of protein consensus sequences by iceLogo. Nat. Methods, 6, 786-787.
16. Cortes, C. and Vapnik, V. (1995) Support-vector networks. Mach. Learn., 20, 273-297.
17. Dix, M.M. et al. (2008) Global mapping of the topography and magnitude of proteolytic events in apoptosis. Cell, 134, 679-691.
18. Dix, M.M. et al. (2012) Functional interplay between caspase cleavage and phosphorylation sculpts the apoptotic proteome. Cell, 150, 426-440.
19. duVerle, D.A. and Mamitsuka, H. (2012) A review of statistical methods for prediction of proteolytic cleavage. Briefings Bioinformatics, 13, 337-349.
20. Enoksson, M. and Salvesen, G.S. (2008) Proteolytic needles in the cellular haystack. Nat. Chem. Biol., 4, 651-652.
21. Enoksson, M. et al. (2007) Identification of proteolytic cleavage sites by quantitative proteomics. J. Proteome Res., 6, 2850-2858.
22. Fischer, U. et al. (2003) Many cuts to ruin: a comprehensive update of caspase substrates. Cell Death Differ., 10, 76-100.
23. Garay-Malpartida, H.M. et al. (2005) CaSPredictor: a new computer-based tool for caspase substrate prediction. Bioinformatics, 21 (Suppl. 1), i169-i176.
24. Gasteiger, E. et al. (2005) Protein identification and analysis tools on the ExPASy server. In: Walker, J.M. (ed.) The Proteomics Protocols Handbook. Humana Press, Totowa, New Jersey, pp. 571-607.
25. Gromiha, M.M. et al. (2010) Sequence and structural analysis of binding site residues in protein-protein complexes. Int. J. Biol. Macromol., 46, 187-192.
26. Grundmann, U. et al. (1988) Characterization of cDNA encoding human placental anticoagulant protein (PP4): homology with the lipocortin family. Proc. Natl Acad. Sci. USA, 85, 3708-3712.
27. Huang, D.S. et al. (2006) Classifying protein sequences using hydropathy blocks. Pattern Recogn., 39, 2293-2300.
28. Huang, Y. et al. (2010) CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics, 26, 680-682.
29. Hunter, S. et al. (2012) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res., 40, D306-D312.
30. Jensen, L.J. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416.
31. Kanehisa, M. and Goto, S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28, 27-30.
32. Kawashima, S. et al. (1999) AAindex: amino acid index database. Nucleic Acids Res., 27, 368-369.
33. Klaiman, G. et al. (2008) Targets of caspase-6 activity in human neurons and Alzheimer disease. Mol. Cell Proteomics, 7, 1541-1555.
34. Kurokawa, M. and Kornbluth, S. (2009) Caspases and kinases in a death grip. Cell, 138, 838-854.
35. Li, B.Q. et al. (2012) Prediction of protein cleavage site with feature selection by random forest. PLoS One, 7, e45854.
36. Li, T. et al. (2010) Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources. PLoS One, 5, e15411.
37. Los, M. et al. (2001) Caspases: more than just killers? Trends Immunol., 22, 31-34.
38. Matthews, B.W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 405, 442-451.
39. Mizianty, M.J. et al. (2010) Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics, 26, i489-i496.
40. Nicholson, D.W. (1999) Caspase structure, proteolytic substrates, and function during apoptotic cell death. Cell Death Differ, 6, 1028-1042.
41. Pardo, J. et al. (2009) The biology of cytotoxic cell granule exocytosis pathway: granzymes have evolved to induce cell death and inflammation. Microbes Infect., 11, 452-459.
42. Peng, H. et al. (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 27, 1226-1238.
43. Piippo, M. et al. (2010) Pripper: prediction of caspase cleavage sites from whole proteomes. BMC Bioinformatics, 11, 320.
44. Punta, M. et al. (2012) The Pfam protein families database. Nucleic Acids Res., 40, D290-D301.
45. Rawlings, N.D. et al. (2008) MEROPS: the peptidase database. Nucleic Acids Res., 36, D320-D325.
46. Realini, C. et al. (1997) Characterization of recombinant REGalpha, REGbeta, and REGgamma proteasome activators. J. Biol. Chem., 272, 25483-25492.
47. Russell, J.H. and Ley, T.J. (2002) Lymphocyte-mediated cytotoxicity. Annu. Rev. Immunol., 20, 323-370.
48. Saeys, Y. et al. (2007) A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517.
49. Schilling, O. and Overall, C.M. (2008) Proteome-derived, database-searchable peptide libraries for identifying protease cleavage sites. Nat. Biotechnol., 26, 685-694.
50. Song, J. et al. (2007) Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure. Bioinformatics, 23, 3147-3154.
51. Song, J. et al. (2010) Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics, 26, 752-760.
52. Song, J. et al. (2011) Bioinformatic approaches for predicting substrates of proteases. J. Bioinform. Comput. Biol., 9, 149-178.
53. Song, J. et al. (2012) PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites. PLoS One, 7, e50300.
54. Stajich, J.E. et al. (2002) The Bioperl toolkit: perl modules for the life sciences. Genome Res., 12, 1611-1618.
55. Team, R.D.C. (2011) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Austria.
56. Turk, B. (2006) Targeting proteases: successes, failures and future prospects. Nat. Rev. Drug Discov., 5, 785-799.
57. Turk, B.E. et al. (2001) Determination of protease cleavage site motifs using mixturebased oriented peptide libraries. Nat. Biotechnol., 19, 661-667.
58. Verspurten, J. et al. (2009) SitePredicting the cleavage of proteinase substrates. Trends Biochem. Sci., 34, 319-323.
59. Wagner, M. et al. (2005) Linear regression models for solvent accessibility prediction in proteins. J. Comput. Biol., 12, 355-369.
60. Wang, M. et al. (2012) FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model. PLoS One, 7, e43847.
61. Ward, J.J. et al. (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol., 337, 635-645.
62. Wee, L.J. et al. (2007) CASVM: web server for SVM-based prediction of caspase substrates cleavage sites. Bioinformatics, 23, 3241-3243.
63. Wilk, S. et al. (2000) Properties of the nuclear proteasome activator PA28gamma (REGgamma). Arch. Biochem. Biophys., 383, 265-271.
64. Wilkins, M.R. et al. (1999) Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol., 112, 531-552.
65. Zhao, X.M. et al. (2010) A discriminative approach for identifying domain-domain interactions from protein-protein interactions. Proteins, 78, 1243-1253.
66. Zhao, X.M. et al. (2005) A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Netw., 18, 1019-1028.
67. Zhao, X.M. et al. (2008) Protein classification with imbalanced data. Proteins, 70, 1125-1132.