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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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Abstract

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:UNDER REVIEW

05 Aug 2020Submitted to PROTEINS: Structure, Function, and Bioinformatics
06 Aug 2020Assigned to Editor
06 Aug 2020Submission Checks Completed
14 Sep 2020Reviewer(s) Assigned