Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. (29th August 2018)

• Record Type: Journal Article
• Authors: Li, Fuyi; Wang, Yanan; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Rawlings, Neil D; Haffari, Gholamreza; Revote, Jerico; Akutsu, Tatsuya; Chou, Kuo-Chen; Purcell, Anthony W; Pike, Robert N; Webb, Geoffrey I; Ian Smith, A; Lithgow, Trevor; Daly, Roger J; Whisstock, James C; Song, Jiangning
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 6(2020)
• Page Start: 2150
• Page End: 2166
• Publication Date: 2018-08-29
• Subjects: protease -- substrate specificity -- substrate cleavage -- bioinformatics -- sequence analysis -- machine learning -- prediction model; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby077
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.