Machine Learning Approaches for Predicting Protein Complex Similarity

dc.contributor.authorFarhoodi, Roshanak
dc.contributor.authorAkbal-Delibas, Bahar
dc.contributor.authorHaspel, Nurit
dc.date.accessioned2019-06-27T08:01:35Z
dc.date.available2019-06-27T08:01:35Z
dc.date.issued2017
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractDiscriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network a multilayer neural network and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small often less than 1.5 angstrom error margin when trained with structures that have RMSD values of up to 7 angstrom. In our most recent experiments with the protein samples having RMSD values up to 27 angstrom the average prediction error was still relatively small attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.en_US]
dc.identifier.citation0
dc.identifier.doi10.1089/cmb.2016.0137en_US
dc.identifier.endpage51
dc.identifier.issn1066-5277en_US
dc.identifier.issn1557-8666en_US
dc.identifier.issn1066-5277
dc.identifier.issn1557-8666
dc.identifier.issue1
dc.identifier.pmid27748625en_US
dc.identifier.scopus2-s2.0-85009060594en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage40en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/412
dc.identifier.urihttps://doi.org/10.1089/cmb.2016.0137
dc.identifier.volume24en_US
dc.identifier.wosWOS:000391761300005en_US
dc.identifier.wosqualityQ2
dc.institutionauthorAkbal-Delibas, Baharen_US
dc.language.isoenen_US
dc.publisherMary Ann Liebert Inc Publen_US
dc.relation.journalJournal of Computational Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectNeural networksen_US
dc.subjectProtein docking and refinementen_US
dc.subjectRMSD predictionen_US
dc.subjectScoring functionsen_US
dc.titleMachine Learning Approaches for Predicting Protein Complex Similarityen_US
dc.typeArticleen_US
dspace.entity.typePublication

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