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Coupling molecular dynamics and deep learning to mine protein conformational space.

Degiacomi, Matteo T. (2019) 'Coupling molecular dynamics and deep learning to mine protein conformational space.', Structure, 27 (6). 1034-1040.e3.

Abstract

Flexibility is often a key determinant of protein func- tion. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational space. Extensive sampling is, however, required to obtain reliable results, useful to rationalize experi- mental data or predict outcomes before experiments are carried out. We demonstrate that a generative neural network trained on protein structures pro- duced by molecular simulation can be used to obtain new, plausible conformations complementing pre- existing ones. To demonstrate this, we show that a trained neural network can be exploited in a pro- tein-protein docking scenario to account for broad hinge motions taking place upon binding. Overall, this work shows that neural networks can be used as an exploratory tool for the study of molecular conformational space.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.str.2019.03.018
Publisher statement:© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Date accepted:25 March 2019
Date deposited:07 June 2019
Date of first online publication:25 April 2019
Date first made open access:07 June 2019

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