Journal Article

Cascleave: towards more accurate prediction of caspase substrate cleavage sites

Jiangning Song, Hao Tan, Hongbin Shen, Khalid Mahmood, Sarah E. Boyd, Geoffrey I. Webb, Tatsuya Akutsu and James C. Whisstock

in Bioinformatics

Volume 26, issue 6, pages 752-760
Published in print March 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI:
Cascleave: towards more accurate prediction of caspase substrate cleavage sites

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Motivation: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates.

Results: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P1 Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship.


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Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6995 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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