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

Jiangning Song, Fuyi Li, Andre Leier, Tatiana Marquez-Lago, Tatsuya Akutsu, Gholamreza Haffari, Kuo-Chen Chou, Geoffrey Ian Bawtree Webb, Robert Neil Pike

Research output: Contribution to journalArticleResearchpeer-review

79 Citations (Scopus)


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 Contact or or Supplementary informationSupplementary dataare available at Bioinformatics online.

Original languageEnglish
Pages (from-to)684-687
Number of pages4
Issue number4
Publication statusPublished - 15 Feb 2018

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