Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features

Author:

Bao Yu1,Marini Simone2,Tamura Takeyuki1,Kamada Mayumi3,Maegawa Shingo4,Hosokawa Hiroshi4,Song Jiangning5ORCID,Akutsu Tatsuya1

Affiliation:

1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan

2. Department of Computational Medicine and Bioinformatics, University of Michigan, 1241 E. Catherine St., 5940 Buhl, Ann Arbor 48109-5618, USA

3. Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8507, Japan

4. Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan

5. Monash Biomedicine Discovery Institute, Monash Centre for Data Science and ARC Centre of Excellence in Advance Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia

Abstract

AbstractAs 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.

Funder

JSPS

National Health and Medical Research Council of Australia

Australian Research Council

National Institute of Allergy and Infectious Diseases of the National Institute of Health

Major Inter-Disciplinary Research (IDR) project

Collaborative Research Program of Institute for Chemical Research, Kyoto University

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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