Ranking Protein-Protein Binding Using Evolutionary Information and Machine Learning

dc.contributor.author Farhoodi, Roshanak
dc.contributor.author Delıbaş, Ayşe Bahar
dc.contributor.author Akbal-Delibas, Bahar
dc.contributor.author Haspel, Nurit
dc.contributor.other Computer Engineering
dc.date.accessioned 2019-06-27T08:01:26Z
dc.date.available 2019-06-27T08:01:26Z
dc.date.issued 2017
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü en_US
dc.description.abstract Discriminating native-like complexes from false-positives with high accuracy is one of the biggest challenges in protein-protein docking. The relationship between various favorable intermolecular interactions (e.g. Van derWaals electrostatic desolvation forces etc.) and the similarity of a conformation to its native structure is commonly agreed though the precise nature of this relationship is not known very well. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and tune their weights by introducing a training set with which they evaluate and rank candidate complexes. Despite improvements in recent docking methods they are still producing a large number of false positives which often leads to incorrect prediction of complex binding. Using machine learning we implemented an approach that not only ranks candidate complexes relative to each other but also predicts how similar each candidate is to the native conformation. We built a Support Vector Regressor (SVR) using physico-chemical features and evolutionary conservation. We trained and tested the model on extensive datasets of complexes generated by three state-of-the-art docking methods. The set of docked complexes was generated from 79 different protein-protein complexes in both the rigid and medium categories of the Protein-Protein Docking Benchmark v.5. We were able to generally outperform the built-in scoring functions of the docking programs we used to generate the complexes attesting to the potential of our approach in predicting the correct binding of protein-protein complexes. en_US]
dc.identifier.citationcount 0
dc.identifier.doi 10.1145/3107411.3107497 en_US
dc.identifier.endpage 672
dc.identifier.isbn 978-1-4503-4722-8
dc.identifier.scopus 2-s2.0-85031324341 en_US
dc.identifier.startpage 667 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/383
dc.identifier.uri https://doi.org/10.1145/3107411.3107497
dc.identifier.wos WOS:000426494700141 en_US
dc.institutionauthor Akbal-Delibas, Bahar en_US
dc.language.iso en en_US
dc.publisher Association for Computing Machinery en_US
dc.relation.journal Proceedings of the 8th ACM International Conference on Bioinformatics en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 0
dc.subject Protein-Protein docking en_US
dc.subject Machine learning en_US
dc.subject Evolutionary conservation en_US
dc.subject SVR en_US
dc.title Ranking Protein-Protein Binding Using Evolutionary Information and Machine Learning en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
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