Skip to main content

Research Repository

Advanced Search

Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations

Ramaswamy, Venkata K.; Musson, Samuel C.; Willcocks, Chris G.; Degiacomi, Matteo T.

Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations Thumbnail


Authors

Venkata K. Ramaswamy



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.

Citation

Ramaswamy, V. K., Musson, S. C., Willcocks, C. G., & Degiacomi, M. T. (2021). Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations. Physical Review X, 11(1), Article 011052. https://doi.org/10.1103/physrevx.11.011052

Journal Article Type Article
Acceptance Date Jan 26, 2021
Online Publication Date Mar 15, 2021
Publication Date 2021-03
Deposit Date Mar 20, 2021
Publicly Available Date Oct 22, 2021
Journal Physical Review X
Publisher American Physical Society
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Article Number 011052
DOI https://doi.org/10.1103/physrevx.11.011052

Files

Published Journal Article (3.4 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright 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.







You might also like



Downloadable Citations