title: Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets.
abstract: BACKGROUND
  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.
  
  
  METHODS
  http://www.structbioinfor.org/cascleave2/.
authors.affiliation: National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan and ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Melbourne, Victoria 3800, Australia.
authors.forename: Mingjun
authors.initials: M
authors.lastname: Wang
authors.forename: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
authors.forename: Hao
authors.initials: H
authors.lastname: Tan
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
authors.forename: James C
authors.initials: JC
authors.lastname: Whisstock
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
mesh_terms: D000595:Amino Acid Sequence; D020169:Caspases / Q000737:chemistry / Q000378:metabolism*; D053804:Granzymes / Q000378:metabolism*; D006801:Humans; D011336:Probability; D020543:Proteome / Q000378:metabolism; D012985:Software Design; D013379:Substrate Specificity
chemical_list: D020543:Proteome; D053804:Granzymes; D020169:Caspases