Accurate Refinement of Docked Protein Complexes Using Evolutionary Information and Deep Learning

dc.contributor.authorAkbal-Delibas, Bahar
dc.contributor.authorFarhoodi, Roshanak
dc.contributor.authorPomplun, Marc
dc.contributor.authorHaspel, Nurit
dc.date.accessioned2019-06-27T08:01:46Z
dc.date.available2019-06-27T08:01:46Z
dc.date.issued2016
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractOne of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work we introduced AccuRefiner a machine learning based tool for refining protein-protein complexes. Given a docked complex the refinement tool produces a small set of refined versions of the input complex with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 angstrom error margin on average by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.en_US]
dc.identifier.citation11
dc.identifier.doi10.1142/S0219720016420026en_US
dc.identifier.issn0219-7200en_US
dc.identifier.issn1757-6334en_US
dc.identifier.issn0219-7200
dc.identifier.issn1757-6334
dc.identifier.issue3
dc.identifier.pmid26846813en_US
dc.identifier.scopus2-s2.0-84957705357en_US
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://hdl.handle.net/20.500.12469/464
dc.identifier.urihttps://doi.org/10.1142/S0219720016420026
dc.identifier.volume14en_US
dc.identifier.wosWOS:000384031500004en_US
dc.institutionauthorAkbal-Delibas, Baharen_US
dc.institutionauthorDelıbaş, Ayşe Bahar
dc.language.isoenen_US
dc.publisherImperıal College Pressen_US
dc.relation.journalJournal of Bioinformatics and Computational Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectProtein Dockingen_US
dc.subjectRanking And Scoring Functionsen_US
dc.subjectDeep Learning Neural Networksen_US
dc.titleAccurate Refinement of Docked Protein Complexes Using Evolutionary Information and Deep Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication229c2e99-3e3a-429f-bc29-b66887aeacda
relation.isAuthorOfPublication.latestForDiscovery229c2e99-3e3a-429f-bc29-b66887aeacda

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