Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features. (31st May 2018)

• Record Type: Journal Article
• Authors: Bao, Yu; Marini, Simone; Tamura, Takeyuki; Kamada, Mayumi; Maegawa, Shingo; Hosokawa, Hiroshi; Song, Jiangning; Akutsu, Tatsuya
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 5(2020)
• Page Start: 1669
• Page End: 1684
• Publication Date: 2018-05-31
• Subjects: caspase -- cleavage sites -- prediction tool; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby041
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized. To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction.