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

dc.contributor.author Akbal-Delibas, Bahar
dc.contributor.author Delıbaş, Ayşe Bahar
dc.contributor.author Farhoodi, Roshanak
dc.contributor.author Pomplun, Marc
dc.contributor.author Haspel, Nurit
dc.contributor.other Computer Engineering
dc.date.accessioned 2019-06-27T08:01:46Z
dc.date.available 2019-06-27T08:01:46Z
dc.date.issued 2016
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü en_US
dc.description.abstract One 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.citationcount 11
dc.identifier.doi 10.1142/S0219720016420026 en_US
dc.identifier.issn 0219-7200 en_US
dc.identifier.issn 1757-6334 en_US
dc.identifier.issn 0219-7200
dc.identifier.issn 1757-6334
dc.identifier.issue 3
dc.identifier.pmid 26846813 en_US
dc.identifier.scopus 2-s2.0-84957705357 en_US
dc.identifier.scopusquality Q3
dc.identifier.uri https://hdl.handle.net/20.500.12469/464
dc.identifier.uri https://doi.org/10.1142/S0219720016420026
dc.identifier.volume 14 en_US
dc.identifier.wos WOS:000384031500004 en_US
dc.institutionauthor Akbal-Delibas, Bahar en_US
dc.language.iso en en_US
dc.publisher Imperıal College Press en_US
dc.relation.journal Journal of Bioinformatics and Computational Biology en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 14
dc.subject Protein Docking en_US
dc.subject Ranking And Scoring Functions en_US
dc.subject Deep Learning Neural Networks en_US
dc.title Accurate Refinement of Docked Protein Complexes Using Evolutionary Information and Deep Learning en_US
dc.type Article en_US
dc.wos.citedbyCount 11
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