Sequence-derived structural features driving proteolytic processing

Proteomics

Volumn 14, Issue 1, 2014, Pages 42-50

  Belushkin, Alexander A ^a   Vinogradov, Dmitry V ^b   Gelfand, Mikhail S ^a,b   Osterman, Andrei L ^c   Cieplak, Piotr ^c   Kazanov, Marat D ^b  

^a MOSCOW STATE UNIVERSITY   (Russian Federation)

^b INSTITUTE FOR INFORMATION TRANSMISSION PROBLEMS   (Russian Federation)

^c BURNHAM INSTITUTE   (United States)

Author keywords

Bioinformatics; Cleavage site; Limited proteolysis; Protease; Proteolytic processing; Regulated proteolysis

Indexed keywords

POLYPEPTIDE; SOLVENT;

ALPHA HELIX; AMINO ACID SEQUENCE; AREA UNDER THE CURVE; ARTICLE; BETA SHEET; BIOINFORMATICS; CORRELATION ANALYSIS; DATA BASE; ENZYME SUBSTRATE; NONHUMAN; PRIORITY JOURNAL; PROTEIN CLEAVAGE; PROTEIN DEGRADATION; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STRUCTURE ANALYSIS;

BIOINFORMATICS; CLEAVAGE SITE; LIMITED PROTEOLYSIS; PROTEASE; PROTEOLYTIC PROCESSING; REGULATED PROTEOLYSIS;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; MODELS, STATISTICAL; PROTEIN CONFORMATION; PROTEINS; PROTEOLYSIS; ROC CURVE;

EID: 84892403803     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201300416     Document Type: Article

References (38)

1. Barrett, A. J., Rawlings, N. D., Woessner, J. M., Handbook of Proteolytic Enzymes: Cysteine, Serine and Threonine Peptidases. 2, Elsevier Acad. Press, Amsterdam, 2004.
2. Turk, B., Targeting proteases: successes, failures and future prospects. Nat. Rev. Drug Discov. 2006, 5, 785-799.
3. Turk, B., Turk, D., Turk, V., Protease signalling: the cutting edge. EMBO J. 2012, 31, 1630-1643.
4. Lopez-Otin, C., Overall, C. M., Protease degradomics: a new challenge for proteomics. Nat. Rev. Mol. Cell Biol. 2002, 3, 509-519.
5. Quesada, V., Ordóñez, G. R., Sánchez, L. M., Puente, X. S., López-Otín, C., The Degradome database: mammalian proteases and diseases of proteolysis. Nucleic Acids Res. 2009, 37, D239-D243.
6. Puente, X. S., Sanchez, L. M., Overall, C. M., Lopez-Otin, C., Human and mouse proteases: a comparative genomic approach. Nat. Rev. Genet. 2003, 4, 544-558.
7. Kasperkiewicz, P., Gajda, A. D., Dra{ogonek}g, M., Current and prospective applications of non-proteinogenic amino acids in profiling of proteases substrate specificity. Biol. Chem. 2012, 393, 843-851.
8. Van den Berg, B. H. J., Tholey, A., Mass spectrometry-based proteomics strategies for protease cleavage site identification. Proteomics 2012, 12, 516-529.
9. Beynon, R. J., Bond, J. S., Proteolytic Enzymes: A Practical Approach, Oxford University Press, Oxford, New York, 2000.
10. Diamond, S. L., Methods for mapping protease specificity. Curr. Opin. Chem. Biol. 2007, 11, 46-51.
11. Turk, B. E., Huang, L. L., Piro, E. T., Cantley, L. C., Determination of protease cleavage site motifs using mixture-based oriented peptide libraries. Nat. Biotechnol. 2001, 19, 661-667.
12. Boyd, S. E., Pike, R. N., Rudy, G. B., Whisstock, J. C., de la Banda, M., PoPS: a computational tool for modeling and predicting protease specificity. J. Bioinform. Comput. Biol. 2005, 3, 551-585.
13. Backes, C., Kuentzer, J., Lenhof, H. P., Comtesse, N., Meese, E., GraBCas: a bioinformatics tool for score-based prediction of Caspase- and Granzyme B-cleavage sites in protein sequences. Nucleic Acids Res. 2005, 33, W208-W213.
14. Garay-Malpartida, H. M., Occhiucci, J. M., Alves, J., Belizario, J. E., CaSPredictor: a new computer-based tool for caspase substrate prediction. Bioinformatics 2005, 21(Suppl 1), i169-i176.
15. Wee, L. J., Tan, T. W., Ranganathan, S., CASVM: web server for SVM-based prediction of caspase substrates cleavage sites. Bioinformatics 2007, 23, 3241-3243.
16. Timmer, J. C., Zhu, W., Pop, C., Regan, T. et al., Structural and kinetic determinants of protease substrates. Nat. Struct. Mol. Biol. 2009, 16, 1101-1108.
17. Hubbard, S. J., Beynon, R. J., Thornton, J. M., Assessment of conformational parameters as predictors of limited proteolytic sites in native protein structures. Protein Eng. 1998, 11, 349-359.
18. Barkan, D. T., Hostetter, D. R., Mahrus, S., Pieper, U. et al., Prediction of protease substrates using sequence and structure features. Bioinformatics 2010, 26, 1714-1722.
19. Song, J., Tan, H., Shen, H., Mahmood, K. et al., Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics 2010, 26, 752-760.
20. Kazanov, M. D., Igarashi, Y., Eroshkin, A. M., Cieplak, P. et al., Structural determinants of limited proteolysis. J. Proteome Res. 2011, 10, 3642-3651.
21. Igarashi, Y., Eroshkin, A., Gramatikova, S., Gramatikoff, K. et al., CutDB: a proteolytic event database. Nucleic Acids Res. 2007, 35, D546-D549.
22. Zhang, H., Zhang, T., Chen, K., Kedarisetti, K. D. et al., Critical assessment of high-throughput standalone methods for secondary structure prediction. Brief. Bioinform. 2011, 12, 672-688.
23. Adamczak, R., Porollo, A., Meller, J., Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 2005, 59, 467-475.
24. Petersen, B., Petersen, T. N., Andersen, P., Nielsen, M., Lundegaard, C., A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol. 2009, 9, 51.
25. Faraggi, E., Zhang, T., Yang, Y., Kurgan, L., Zhou, Y., SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J. Comput. Chem. 2012, 33, 259-267.
26. Cheng, J., Randall, A. Z., Sweredoski, M. J., Baldi, P., SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res. 2005, 33, W72-W76.
27. Rost, B., Sander, C., Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 1994, 19, 55-72.
28. McGuffin, L. J., Bryson, K., Jones, D. T., The PSIPRED protein structure prediction server. Bioinformatics 2000, 16, 404-405.
29. Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F., Jones, D. T., Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol. 2004, 337, 635-645.
30. Linding, R., Jensen, L. J., Diella, F., Bork, P. et al., Protein disorder prediction: implications for structural proteomics. Structure 2003, 11, 1453-1459.
31. Dosztanyi, Z., Csizmok, V., Tompa, P., Simon, I., IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 2005, 21, 3433-3434.
32. Schechter, I., Berger, A., On the size of the active site in proteases. I. Papain. Biochem. Biophys. Res. Commun. 1967, 27, 157-162.
33. Mahrus, S., Trinidad, J. C., Barkan, D. T., Sali, A. et al., Global sequencing of proteolytic cleavage sites in apoptosis by specific labeling of protein N termini. Cell 2008, 134, 866-876.
34. Massucci, M. T., Giansanti, F., Di Nino, G., Turacchio, M. et al., Proteolytic activity of bovine lactoferrin. Biometals 2004, 17, 249-255.
35. Van Damme, P., Maurer-Stroh, S., Plasman, K., Van Durme, J. et al., Analysis of protein processing by N-terminal proteomics reveals novel species-specific substrate determinants of granzyme B orthologs. Mol. Cell Proteomics 2009, 8, 258-272.
36. Hubbard, S. J., Campbell, S. F., Thornton, J. M., Molecular recognition. Conformational analysis of limited proteolytic sites and serine proteinase protein inhibitors. J. Mol. Biol. 1991, 220, 507-530.
37. Hubbard, S. J., Eisenmenger, F., Thornton, J. M., Modeling studies of the change in conformation required for cleavage of limited proteolytic sites. Protein Sci. 1994, 3, 757-768.
38. Rawlings, N. D., Barrett, A. J., Bateman, A., MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 2012, 40, D343-D350.