Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders.

ABSTRACT: Conformational sampling of biomolecules using molecular dynamics simulations often produces a large amount of high dimensional data that makes it difficult to interpret using conventional analysis techniques. Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE), that can simultaneously perform dimensionality reduction and clustering of biomolecular conformations in an unsupervised way. We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding. Since GMVAE uses a mixture of Gaussians as its prior, it can directly acknowledge the m

SUBMITTER: Ghorbani M 

PROVIDER: S-EPMC8605902 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature