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

Autor:	Li F; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia., Wang Y; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China., Li C; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.; Department of Biology, Institute of Molecular Systems Biology,ETH Zürich, Zürich 8093, Switzerland., Marquez-Lago TT; Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA., Leier A; Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA., Rawlings ND; EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Wellcome Trust Genome Campus,Hinxton, Cambridgeshire CB10 1SD, UK., Haffari G; Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia., Revote J; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia., Akutsu T; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan., Chou KC; Gordon Life Science Institute, Boston, MA 02478, USA.; Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China., Purcell AW; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia., Pike RN; La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC 3086, Australia.; ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia., Webb GI; Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia., Ian Smith A; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.; ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia., Lithgow T; Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, Victoria 3800, Australia., Daly RJ; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia., Whisstock JC; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.; ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia., Song J; Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.; Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.; ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2019 Nov 27; Vol. 20 (6), pp. 2150-2166.
DOI:	10.1093/bib/bby077
Abstrakt:	The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction. (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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