CASVM

web server for SVM-based prediction of caspase substrates cleavage sites

Lawrence J. K. Wee, Tin Wee Tan, Shoba Ranganathan*

*Corresponding author for this work

Research output: Contribution to journalArticle

55 Citations (Scopus)

Abstract

Summary: Caspases belong to a unique class of cysteine proteases which function as critical effectors of apoptosis, inflammation and other important cellular processes. Caspases cleave substrates at specific tetrapeptide sites after a highly conserved aspartic acid residue. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. We have recently developed a support vector machines (SVM) method to address this issue. Our algorithm achieved an accuracy ranging from 81.25 to 97.92%, making it one of the best methods currently available. CASVM is the web server implementation of our SVM algorithms, written in Perl and hosted on a Linux platform. The server can be used for predicting non-canonical caspase substrate cleavage sites. We have also included a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity.

Original languageEnglish
Pages (from-to)3241-3243
Number of pages3
JournalBioinformatics
Volume23
Issue number23
DOIs
Publication statusPublished - Dec 2007

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