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DeepCleave: A deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

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dc.contributor.authorLi, F.
dc.contributor.authorChen, J.
dc.contributor.authorLeier, A.
dc.contributor.authorMarquez-Lago, T.
dc.contributor.authorLiu, Q.
dc.contributor.authorWang, Y.
dc.contributor.authorRevote, J.
dc.contributor.authorSmith, A.I.
dc.contributor.authorAkutsu, T.
dc.contributor.authorWebb, G.I.
dc.contributor.authorKurgan, L.
dc.contributor.authorSong, J.
dc.contributor.editorElofsson, A.
dc.date.issued2020
dc.description.abstractMotivation: Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the ‘life and death’ cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases’ functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. Results: We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. Availability and implementation: The DeepCleave webserver and source code are freely available at http://deep cleave.erc.monash.edu/.
dc.description.statementofresponsibilityFuyi Li, Jinxiang Chen, Andre Leier, Tatiana Marquez-Lago, Quanzhong Liu, Yanze Wang, Jerico Revote, A. Ian Smith, Tatsuya Akutsu, Geoffrey I. Webb, Lukasz Kurgan, and Jiangning Song
dc.identifier.citationBioinformatics, 2020; 36(4):1057-1065
dc.identifier.doi10.1093/bioinformatics/btz721
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139569
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.relation.granthttp://purl.org/au-research/grants/nhmrc/1092262
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/490989
dc.rights© The Author(s) 2019. Published by Oxford University Press. All rights reserved.
dc.source.urihttps://doi.org/10.1093/bioinformatics/btz721
dc.subjectCaspases
dc.subjectMetalloproteases
dc.subjectSubstrate Specificity
dc.subjectSoftware
dc.subjectDeep Learning
dc.subject.meshCaspases
dc.subject.meshMetalloproteases
dc.subject.meshSubstrate Specificity
dc.subject.meshSoftware
dc.subject.meshDeep Learning
dc.titleDeepCleave: A deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites
dc.typeJournal article
pubs.publication-statusPublished

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