Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling.

ABSTRACT: Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to cry

SUBMITTER: Mansoor S 

PROVIDER: S-EPMC11008089 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature