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

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dc.contributor.authorSong, J.
dc.contributor.authorLi, F.
dc.contributor.authorLeier, A.
dc.contributor.authorMarquez-Lago, T.T.
dc.contributor.authorAkutsu, T.
dc.contributor.authorHaffari, G.
dc.contributor.authorChou, K.C.
dc.contributor.authorWebb, G.I.
dc.contributor.authorPike, R.N.
dc.contributor.editorHancock, J.
dc.date.issued2018
dc.description.abstractSummary: 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/
dc.description.statementofresponsibilityJiangning Song, Fuyi Li, André Leier, Tatiana T. Marquez-Lago, Tatsuya Akutsu, Gholamreza Haffari, Kuo-Chen Chou, Geoffrey I. Webb, and Robert N. Pike
dc.identifier.citationBioinformatics, 2018; 34(4):684-687
dc.identifier.doi10.1093/bioinformatics/btx670
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139595
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.granthttp://purl.org/au-research/grants/arc/LP110200333
dc.relation.granthttp://purl.org/au-research/grants/arc/DP120104460
dc.rights© The Author 2017. Published by Oxford University Press. All rights reserved
dc.source.urihttps://doi.org/10.1093/bioinformatics/btx670
dc.subjectPeptide Hydrolases
dc.subjectSequence Analysis, Protein
dc.subjectComputational Biology
dc.subjectSubstrate Specificity
dc.subjectComputer Simulation
dc.subjectSoftware
dc.subjectProteolysis
dc.subjectData Accuracy
dc.subject.meshPeptide Hydrolases
dc.subject.meshSequence Analysis, Protein
dc.subject.meshComputational Biology
dc.subject.meshSubstrate Specificity
dc.subject.meshComputer Simulation
dc.subject.meshSoftware
dc.subject.meshProteolysis
dc.subject.meshData Accuracy
dc.titlePROSPERous: High-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy
dc.typeJournal article
pubs.publication-statusPublished

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