MGPfusion

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Jokinen, Emmi
dc.contributor.author Heinonen, Markus
dc.contributor.author Lähdesmäki, Harri
dc.date.accessioned 2018-08-21T13:48:23Z
dc.date.available 2018-08-21T13:48:23Z
dc.date.issued 2018-07-01
dc.identifier.citation Jokinen , 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 . DOI: 10.1093/bioinformatics/bty238 en
dc.identifier.issn 1367-4803
dc.identifier.issn 1460-2059
dc.identifier.other PURE UUID: f01f0861-0df1-4314-8b9e-3a067a752f3a
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/mgpfusion(f01f0861-0df1-4314-8b9e-3a067a752f3a).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85050799574&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27134509/bty238.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33574
dc.description.abstract Motivation: 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 proteinsby 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.format.extent i274-i283
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Bioinformatics en
dc.relation.ispartofseries Volume 34, issue 13 en
dc.rights openAccess en
dc.subject.other Statistics and Probability en
dc.subject.other Biochemistry en
dc.subject.other Molecular Biology en
dc.subject.other Computer Science Applications en
dc.subject.other Computational Theory and Mathematics en
dc.subject.other Computational Mathematics en
dc.subject.other 113 Computer and information sciences en
dc.title MGPfusion en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Centre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMys
dc.subject.keyword Statistics and Probability
dc.subject.keyword Biochemistry
dc.subject.keyword Molecular Biology
dc.subject.keyword Computer Science Applications
dc.subject.keyword Computational Theory and Mathematics
dc.subject.keyword Computational Mathematics
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201808214707
dc.identifier.doi 10.1093/bioinformatics/bty238
dc.type.version publishedVersion


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