Autor(es): Wee Lawrence J K, Er Esmond P S, Ng Lisa F P, Tong J C

Resumo: Granzyme B is a serine protease which cleaves at unique tetrapeptide sequences. It is involved in several signaling cross-talks with caspases - functions as a pivotal mediator in a broad range of cellular processes such as apoptosis - inflammation. The granzyme B degradome constitutes proteins from a myriad of functional classes with many more expected to be discovered. However, the experimental discovery - validation of bona fide granzyme B substrates require time consuming - laborious efforts. As such, computational methods for the prediction of substrates would be immensely helpful. We have compiled a dataset of 580 experimentally verified granzyme B cleavage sites - found distinctive patterns of residue conservation - 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 - compositions. The SVM classifiers achieved accuracy - AROC scores between 71.00% to 86.50% - 0.78 to 0.94 respectively on independent test sets. We have applied our prediction method on the Chikungunya viral proteome - 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. We have compiled a comprehensive dataset of granzyme B cleavage sites - 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 - suplementary materials.

Imprenta: BMC Genomics, v. 12, supl 3, S11, 2011

Identificador do Objeto Digital: 10.1186/1471-2164-12-S3-S11

Descritores: Chikungunya virus - Biosynthesis ; Chikungunya virus - Proteins ; Chikungunya virus - Proteome ; Chikungunya virus - Inflammation ; Chikungunya Virus - Virus ; Chikungunya virus - Public health

Data de Publicação: 2011