MGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorJokinen, Emmien_US
dc.contributor.authorHeinonen, Markusen_US
dc.contributor.authorLähdesmäki, Harrien_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Lähdesmäki Harrien
dc.contributor.groupauthorCentre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMysen
dc.date.accessioned2018-08-21T13:48:23Z
dc.date.available2018-08-21T13:48:23Z
dc.date.issued2018-07-01en_US
dc.description.abstractMotivation: Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins? properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data. Results: We have developed mGPfusion, a novel Gaussian process (GP) method for predicting protein?s stability changes upon single and multiple mutations. This method complements the limited experimental data with large amounts of molecular simulation data. We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data. Our protein-specific model requires experimental data only regarding the protein of interest and performs well even with few experimental measurements. The mGPfusion models proteins by contact maps and infers the stability effects caused by mutations with a mixture of graph kernels. Our results show that mGPfusion outperforms stateof- the-art methods in predicting protein stability on a dataset of 15 different proteins and that incorporating molecular simulation data improves the model learning and prediction accuracy.en
dc.description.versionPeer revieweden
dc.format.extenti274-i283
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationJokinen, E, Heinonen, M & Lähdesmäki, H 2018, ' MGPfusion : Predicting protein stability changes with Gaussian process kernel learning and data fusion ', Bioinformatics, vol. 34, no. 13, pp. i274-i283 . https://doi.org/10.1093/bioinformatics/bty238en
dc.identifier.doi10.1093/bioinformatics/bty238en_US
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.otherPURE UUID: f01f0861-0df1-4314-8b9e-3a067a752f3aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/f01f0861-0df1-4314-8b9e-3a067a752f3aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85050799574&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/27134509/bty238.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/33574
dc.identifier.urnURN:NBN:fi:aalto-201808214707
dc.language.isoenen
dc.relation.ispartofseriesBioinformaticsen
dc.relation.ispartofseriesVolume 34, issue 13en
dc.rightsopenAccessen
dc.titleMGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusionen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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