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Predicting New Protein Conformations from Molecular Dynamics Simulation Conformational Landscapes and Machine Learning
  • Yiming Jin,
  • Linus Johannissen,
  • Sam Hay
Yiming Jin
Central South University
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Linus Johannissen
The University of Manchester
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Sam Hay
The University of Manchester
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Molecular dynamics (MD) simulations are a popular method of studying protein structure and function, but are unable to reliably sample all relevant conformational space in reasonable computational timescales. A range of enhanced sampling methods are available that can improve conformational sampling, but these do not offer a complete solution. We present here a proof-of-principle method of combining MD simulation with machine learning to explore protein conformational space. An autoencoder is used to map snapshots from MD simulations onto the conformational landscape defined by a 2D-RMSD matrix, and we show that we can predict, with useful accuracy, conformations that are not present in the training data. This method offers a new approach to the prediction of new low energy/physically realistic structures of conformationally dynamic proteins and allows an alternative approach to enhanced sampling of MD simulations.

Peer review status:Published

05 Aug 2020Submitted to PROTEINS: Structure, Function, and Bioinformatics
06 Aug 2020Submission Checks Completed
06 Aug 2020Assigned to Editor
14 Sep 2020Reviewer(s) Assigned
09 Oct 2020Review(s) Completed, Editorial Evaluation Pending
12 Oct 2020Editorial Decision: Revise Major
14 Jan 20211st Revision Received
22 Jan 2021Submission Checks Completed
22 Jan 2021Assigned to Editor
11 Feb 2021Reviewer(s) Assigned
23 Feb 2021Review(s) Completed, Editorial Evaluation Pending
23 Feb 2021Editorial Decision: Accept
25 Feb 2021Published in Proteins: Structure, Function, and Bioinformatics. 10.1002/prot.26068