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

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

Content Provider	ACM Digital Library
Author	Pomplun, Marc Haspel, Nurit Akbal-Delibas, Bahar
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.
Starting Page	289
Ending Page	296
Page Count	8
File Format	PDF
ISBN	9781450328944
DOI	10.1145/2649387.2649392
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Protein docking and refinement Neural networks Rmsd prediction Machine learning Scoring functions
Content Type	Text
Resource Type	Article

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