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