SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

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dc.contributor.authorWang, X.
dc.contributor.authorLi, C.
dc.contributor.authorLi, F.
dc.contributor.authorSharma, V.S.
dc.contributor.authorSong, J.
dc.contributor.authorWebb, G.I.
dc.date.issued2019
dc.description.abstractBackground: S-sulphenylation is a ubiquitous protein post-translational modification (PTM) where an S-hydroxyl (-SOH) bond is formed via the reversible oxidation on the Sulfhydryl group of cysteine (C). Recent experimental studies have revealed that S-sulphenylation plays critical roles in many biological functions, such as protein regulation and cell signaling. State-of-the-art bioinformatic advances have facilitated high-throughput in silico screening of protein S-sulphenylation sites, thereby significantly reducing the time and labour costs traditionally required for the experimental investigation of S-sulphenylation. Results:In this study, we have proposed a novel hybrid computational framework, termed SIMLIN, for accurate prediction of protein S-sulphenylation sites using a multi-stage neural-network based ensemble-learning model integrating both protein sequence derived and protein structural features. Benchmarking experiments against the current state-of-the-art predictors for S-sulphenylation demonstrated that SIMLIN delivered competitive prediction performance. The empirical studies on the independent testing dataset demonstrated that SIMLIN achieved 88.0% prediction accuracy and an AUC score of 0.82, which outperforms currently existing methods. Conclusions: In summary, SIMLIN predicts human S-sulphenylation sites with high accuracy thereby facilitating biological hypothesis generation and experimental validation. The web server, datasets, and online instructions are freely available at http://simlin.erc.monash.edu/ for academic purposes.
dc.description.statementofresponsibilityXiaochuan Wang, Chen Li, Fuyi Li, Varun S. Sharma, Jiangning Song, and Geoffrey I. Webb
dc.identifier.citationBMC Bioinformatics, 2019; 20(1)
dc.identifier.doi10.1186/s12859-019-3178-6
dc.identifier.issn1471-2105
dc.identifier.issn1471-2105
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139591
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.granthttp://purl.org/au-research/grants/arc/DP120104460
dc.relation.granthttp://purl.org/au-research/grants/arc/DP120104460
dc.relation.granthttp://purl.org/au-research/grants/arc/LP110200333
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1144652
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/490989
dc.rights© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.source.urihttps://doi.org/10.1186/s12859-019-3178-6
dc.subjectProtein post-translational modification; S-sulphenylation; Bioinformatics software; Machine learning; Ensemble learning
dc.subject.meshHumans
dc.subject.meshSulfamerazine
dc.subject.meshProteome
dc.subject.meshArea Under Curve
dc.subject.meshROC Curve
dc.subject.meshComputational Biology
dc.subject.meshAmino Acid Sequence
dc.subject.meshAmino Acid Motifs
dc.subject.meshConserved Sequence
dc.subject.meshAlgorithms
dc.subject.meshSoftware
dc.subject.meshDatabases, Protein
dc.subject.meshGene Ontology
dc.subject.meshNeural Networks, Computer
dc.titleSIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models
dc.typeJournal article
pubs.publication-statusPublished

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