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short-paper

AccuRMSD: a machine learning approach to predicting structure similarity of docked protein complexes

Authors:

Bahar Akbal-Delibas,

Nurit HaspelAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 289 - 296

https://doi.org/10.1145/2649387.2649392

Published: 20 September 2014 Publication History

Abstract

Protein-protein docking methods aim to compute the correct bound form of two or more proteins. One of the major challenges for docking methods is to accurately discriminate native-like structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against a specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives.

Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm and achieved RMSD prediction accuracy with less than 1Å error margin on 19,600 test samples.

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Cited By

Farhoodi RAkbal-Delibas BHaspel NHaspel NCowen LShehu AKahveci TPozzi G(2017)Ranking Protein-Protein Binding Using Evolutionary Information and Machine LearningProceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics10.1145/3107411.3107497(667-672)Online publication date: 20-Aug-2017
https://dl.acm.org/doi/10.1145/3107411.3107497
Farhoodi RAkbal-Delibas BHaspel N(2017)Machine Learning Approaches for Predicting Protein Complex SimilarityJournal of Computational Biology10.1089/cmb.2016.013724:1(40-51)Online publication date: Jan-2017
https://doi.org/10.1089/cmb.2016.0137
Haspel NJagodzinski F(2016)Methods for Detecting Critical Residues in ProteinsIn Vitro Mutagenesis10.1007/978-1-4939-6472-7_15(227-242)Online publication date: 6-Oct-2016
https://doi.org/10.1007/978-1-4939-6472-7_15
Show More Cited By

Index Terms

AccuRMSD: a machine learning approach to predicting structure similarity of docked protein complexes

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Published In

cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

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Division of Computing and Communication Foundations

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BCB '14

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SIGBio

BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Farhoodi RAkbal-Delibas BHaspel NHaspel NCowen LShehu AKahveci TPozzi G(2017)Ranking Protein-Protein Binding Using Evolutionary Information and Machine LearningProceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics10.1145/3107411.3107497(667-672)Online publication date: 20-Aug-2017
https://dl.acm.org/doi/10.1145/3107411.3107497
Farhoodi RAkbal-Delibas BHaspel N(2017)Machine Learning Approaches for Predicting Protein Complex SimilarityJournal of Computational Biology10.1089/cmb.2016.013724:1(40-51)Online publication date: Jan-2017
https://doi.org/10.1089/cmb.2016.0137
Haspel NJagodzinski F(2016)Methods for Detecting Critical Residues in ProteinsIn Vitro Mutagenesis10.1007/978-1-4939-6472-7_15(227-242)Online publication date: 6-Oct-2016
https://doi.org/10.1007/978-1-4939-6472-7_15
Farhoodi RAkbal-Delibas BHaspel N(2015)Accurate prediction of docked protein structure similarity using neural networks and restricted Boltzmann machinesProceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2015.7359866(1296-1303)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/BIBM.2015.7359866
Akbal-Delibas BPomplun MHaspel N(2015)Accurate Prediction of Docked Protein Structure SimilarityJournal of Computational Biology10.1089/cmb.2015.011422:9(892-904)Online publication date: Sep-2015
https://doi.org/10.1089/cmb.2015.0114

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