Literature DB >> 24149049

Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets.

Mingjun Wang1, Xing-Ming Zhao, Hao Tan, Tatsuya Akutsu, James C Whisstock, Jiangning Song.   

Abstract

MOTIVATION: Caspases and granzyme B (GrB) are important proteases involved in fundamental cellular processes and play essential roles in programmed cell death, necrosis and inflammation. Although a number of substrates for both types have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remained to be fully characterized. Accordingly, systematic bioinformatics studies of known cleavage sites may provide important insights into their substrate specificity and facilitate the discovery of novel substrates.
RESULTS: We develop a new bioinformatics tool, termed Cascleave 2.0, which builds on previous success of the Cascleave tool for predicting generic caspase cleavage sites. It can be efficiently used to predict potential caspase-specific cleavage sites for the human caspase-1, 3, 6, 7, 8 and GrB. In particular, we integrate heterogeneous sequence and protein functional information from various sources to improve the prediction accuracy of Cascleave 2.0. During classification, we use both maximum relevance minimum redundancy and forward feature selection techniques to quantify the relative contribution of each feature to prediction and thus remove redundant as well as irrelevant features. A systematic evaluation of Cascleave 2.0 using the benchmark data and comparison with other state-of-the-art tools using independent test data indicate that Cascleave 2.0 outperforms other tools on protease-specific cleavage site prediction of caspase-1, 3, 6, 7 and GrB. Cascleave 2.0 is anticipated to be used as a powerful tool for identifying novel substrates and cleavage sites of caspases and GrB and help understand the functional roles of these important proteases in human proteolytic cascades.
AVAILABILITY AND IMPLEMENTATION: http://www.structbioinfor.org/cascleave2/.

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Year:  2013        PMID: 24149049     DOI: 10.1093/bioinformatics/btt603

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  30 in total

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4.  Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

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5.  PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

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7.  DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.

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10.  A proteasome-resistant fragment of NIK mediates oncogenic NF-κB signaling in schwannomas.

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Journal:  Hum Mol Genet       Date:  2019-02-15       Impact factor: 6.150

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