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Title: In silico prediction of the granzyme B degradome
Authors: Wee, L.J.K
Er, E.P.S
Ng, L.F.P 
Tong, J.C 
Keywords: Antiviral activities
Cellular process
Cleavage sites
Data sets
Functional class
Granzyme B
Innate immune response
Potential sites
Prediction methods
Regulatory domain
Serine protease
SVM classifiers
Test sets
Tetrapeptide sequence
Viral proteins
Amino acids
Cell death
Classification (of information)
Feature extraction
Learning algorithms
Support vector machines
virus protein
Bayes theorem
Chikungunya alphavirus
factual database
support vector machine
Bayes Theorem
Chikungunya virus
Computational Biology
Databases, Factual
Support Vector Machines
Viral Proteins
Issue Date: 2011
Citation: Wee, L.J.K, Er, E.P.S, Ng, L.F.P, Tong, J.C (2011). In silico prediction of the granzyme B degradome. 10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet) 12 (SUPPL. 3) : S11. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Background: Granzyme B is a serine protease which cleaves at unique tetrapeptide sequences. It is involved in several signaling cross-talks with caspases and functions as a pivotal mediator in a broad range of cellular processes such as apoptosis and inflammation. The granzyme B degradome constitutes proteins from a myriad of functional classes with many more expected to be discovered. However, the experimental discovery and validation of bona fide granzyme B substrates require time consuming and laborious efforts. As such, computational methods for the prediction of substrates would be immensely helpful. Results: We have compiled a dataset of 580 experimentally verified granzyme B cleavage sites and found distinctive patterns of residue conservation and position-specific residue propensities which could be useful for in silico prediction using machine learning algorithms. We trained a series of support vector machines (SVM) classifiers employing Bayes Feature Extraction to predict cleavage sites using sequence windows of diverse lengths and compositions. The SVM classifiers achieved accuracy and AROC scores between 71.00% to 86.50% and 0.78 to 0.94 respectively on independent test sets. We have applied our prediction method on the Chikungunya viral proteome and identified several regulatory domains of viral proteins to be potential sites of granzyme B cleavage, suggesting direct antiviral activity of granzyme B during host-viral innate immune responses. Conclusions: We have compiled a comprehensive dataset of granzyme B cleavage sites and developed an accurate SVM-based prediction method utilizing Bayes Feature Extraction to identify novel substrates of granzyme B in silico. The prediction server is available online, together with reference datasets and supplementary materials. © 2011 licensee BioMed Central Ltd.
Source Title: 10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)
DOI: 10.1186/1471-2164-12-S3-S11
Rights: Attribution 4.0 International
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