PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy

• Published in: Bioinformatics Vol. 34; no. 4; pp. 684 - 687
• Main Authors: Song, Jiangning, Li, Fuyi, Leier, André, Marquez-Lago, Tatiana T, Akutsu, Tatsuya, Haffari, Gholamreza, Chou, Kuo-Chen, Webb, Geoffrey I, Pike, Robert N
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15-02-2018
• Subjects: Applications Notes; Computational Biology - methods; Computer Simulation; Data Accuracy; Peptide Hydrolases - metabolism; Proteolysis; Sequence Analysis, Protein - methods; Software; Substrate Specificity
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btx670

Abstract
Summary
Proteases are enzymes that specifically cleave the peptide backbone of their target proteins. As an important type of irreversible post-translational modification, protein cleavage underlies many key physiological processes. When dysregulated, proteases' actions are associated with numerous diseases. Many proteases are highly specific, cleaving only those target substrates that present certain particular amino acid sequence patterns. Therefore, tools that successfully identify potential target substrates for proteases may also identify previously unknown, physiologically relevant cleavage sites, thus providing insights into biological processes and guiding hypothesis-driven experiments aimed at verifying protease-substrate interaction. In this work, we present PROSPERous, a tool for rapid in silico prediction of protease-specific cleavage sites in substrate sequences. Our tool is based on logistic regression models and uses different scoring functions and their pairwise combinations to subsequently predict potential cleavage sites. PROSPERous represents a state-of-the-art tool that enables fast, accurate and high-throughput prediction of substrate cleavage sites for 90 proteases.
Availability and implementation
http://prosperous.erc.monash.edu/
Supplementary information
Supplementary data are available at Bioinformatics online.