Quokka: A comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome

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dc.contributor.authorLi, F.
dc.contributor.authorLi, C.
dc.contributor.authorMarquez-Lago, T.T.
dc.contributor.authorLeier, A.
dc.contributor.authorAkutsu, T.
dc.contributor.authorPurcell, A.W.
dc.contributor.authorSmith, A.I.
dc.contributor.authorLithgow, T.
dc.contributor.authorDaly, R.J.
dc.contributor.authorSong, J.
dc.contributor.authorChou, K.C.
dc.contributor.editorHancock, J.
dc.date.issued2018
dc.description.abstractMotivation: Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and proteinprotein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase familyspecific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. Results: In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. Availability and implementation: The Quokka webserver and datasets are freely available at http:// quokka.erc.monash.edu/.
dc.description.statementofresponsibilityFuyi Li, Chen Li, Tatiana T. Marquez-Lago, Andre, Leier, Tatsuya Akutsu, Anthony W. Purcell, A. Ian Smith, Trevor Lithgow, Roger J. Daly, Jiangning Song, and Kuo-Chen Chou
dc.identifier.citationBioinformatics, 2018; 34(24):4223-4231
dc.identifier.doi10.1093/bioinformatics/bty522
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139592
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/490989
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/130100038
dc.rights© The Author(s) 2018. Published by Oxford University Press. All rights reserved.
dc.source.urihttps://doi.org/10.1093/bioinformatics/bty522
dc.subjectAnimals
dc.subjectHumans
dc.subjectProteome
dc.subjectProteomics
dc.subjectProtein Processing, Post-Translational
dc.subjectPhosphorylation
dc.subjectAlgorithms
dc.subject.meshAnimals
dc.subject.meshHumans
dc.subject.meshProteome
dc.subject.meshProteomics
dc.subject.meshProtein Processing, Post-Translational
dc.subject.meshPhosphorylation
dc.subject.meshAlgorithms
dc.titleQuokka: A comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome
dc.typeJournal article
pubs.publication-statusPublished

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