Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-12-S3-S11
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dc.titleIn silico prediction of the granzyme B degradome
dc.contributor.authorWee, L.J.K
dc.contributor.authorEr, E.P.S
dc.contributor.authorNg, L.F.P
dc.contributor.authorTong, J.C
dc.date.accessioned2020-10-27T11:29:36Z
dc.date.available2020-10-27T11:29:36Z
dc.date.issued2011
dc.identifier.citationWee, 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
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/181619
dc.description.abstractBackground: 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.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectAntiviral activities
dc.subjectCaspases
dc.subjectCellular process
dc.subjectChikungunya
dc.subjectCleavage sites
dc.subjectData sets
dc.subjectFunctional class
dc.subjectGranzyme B
dc.subjectIn-silico
dc.subjectInnate immune response
dc.subjectPotential sites
dc.subjectPrediction methods
dc.subjectRegulatory domain
dc.subjectSerine protease
dc.subjectSVM classifiers
dc.subjectTest sets
dc.subjectTetrapeptide sequence
dc.subjectViral proteins
dc.subjectAmino acids
dc.subjectCell death
dc.subjectClassification (of information)
dc.subjectFeature extraction
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectProteins
dc.subjectSupport vector machines
dc.subjectBioinformatics
dc.subjectgranzyme
dc.subjectproteome
dc.subjectvirus protein
dc.subjectarticle
dc.subjectBayes theorem
dc.subjectbiology
dc.subjectChikungunya alphavirus
dc.subjectfactual database
dc.subjectmetabolism
dc.subjectsupport vector machine
dc.subjectBayes Theorem
dc.subjectChikungunya virus
dc.subjectComputational Biology
dc.subjectDatabases, Factual
dc.subjectGranzymes
dc.subjectProteome
dc.subjectSupport Vector Machines
dc.subjectViral Proteins
dc.typeConference Paper
dc.contributor.departmentDEPT OF BIOCHEMISTRY
dc.description.doi10.1186/1471-2164-12-S3-S11
dc.description.sourcetitle10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)
dc.description.volume12
dc.description.issueSUPPL. 3
dc.description.pageS11
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