Predicting New Protein Conformations from Molecular Dynamics Simulation Conformational Landscapes and Machine Learning

Yiming Jin , Linus Johannissen, Sam Hay

Research output: Contribution to journalArticlepeer-review

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 a user-defined conformational landscape defined by principal components analysis or specific structural features, 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.
Original languageEnglish
JournalProteins: structure, function, and bioinformatics
Early online date25 Feb 2021
DOIs
Publication statusE-pub ahead of print - 25 Feb 2021

Research Beacons, Institutes and Platforms

  • Manchester Institute of Biotechnology

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