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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 Caspases Cellular process Chikungunya Cleavage sites Data sets Functional class Granzyme B In-silico 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 Forecasting Learning algorithms Proteins Support vector machines Bioinformatics granzyme proteome virus protein article Bayes theorem biology Chikungunya alphavirus factual database metabolism support vector machine Bayes Theorem Chikungunya virus Computational Biology Databases, Factual Granzymes Proteome 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. https://doi.org/10.1186/1471-2164-12-S3-S11 | 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) | URI: | https://scholarbank.nus.edu.sg/handle/10635/181619 | DOI: | 10.1186/1471-2164-12-S3-S11 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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