Please use this identifier to cite or link to this item:
|Title:||SVM-based prediction of caspase substrate cleavage sites||Authors:||Wee, L.J.K.
|Issue Date:||2006||Citation:||Wee, L.J.K., Tan, T.W., Ranganathan, S. (2006). SVM-based prediction of caspase substrate cleavage sites. BMC Bioinformatics 7 (SUPPL.5) : S14-. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-7-S5-S14||Abstract:||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 tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. CONCLUSION: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.||Source Title:||BMC Bioinformatics||URI:||http://scholarbank.nus.edu.sg/handle/10635/28661||ISSN:||14712105||DOI:||10.1186/1471-2105-7-S5-S14|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
|2006-SVM-based_prediction_caspase_substrate_cleavage-published.pdf||290.97 kB||Adobe PDF|
checked on Apr 24, 2019
WEB OF SCIENCETM
checked on Apr 23, 2019
checked on Apr 21, 2019
checked on Apr 21, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.