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Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations

Ramaswamy, Venkata K. and Musson, Samuel C. and Willcocks, Chris G. and Degiacomi, Matteo T. (2021) 'Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations.', Physical review X., 11 (1). 011052.

Abstract

Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. This can be accomplished by sampling protein conformational space with molecular simulation methodologies. Despite advances in computing hardware and sampling techniques, simulations always yield a discretized representation of this space, with transition states undersampled proportionally to their associated energy barrier. We present a convolutional neural network that learns a continuous conformational space representation from example structures, and loss functions that ensure intermediates between examples are physically plausible. We show that this network, trained with simulations of distinct protein states, can correctly predict a biologically relevant transition path, without any example on the path provided. We also show we can transfer features learned from one protein to others, which results in superior performances, and requires a surprisingly small number of training examples.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1103/PhysRevX.11.011052
Publisher statement:Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Date accepted:26 January 2021
Date deposited:22 October 2021
Date of first online publication:15 March 2021
Date first made open access:22 October 2021

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