SVM-based prediction of caspase substrate cleavage sites

Background: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tet...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Lawrence J. K. Wee, Tin Wee Tan, Shoba Ranganathan
• Keywords: BMCBI 2006, Cleavage Sites, Substrates, Tetrapeptide Cleavage Sites