Generating tertiary protein structures via interpretable graph variational autoencoders.

ABSTRACT:

Motivation

Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Sinc

SUBMITTER: Guo X 

PROVIDER: S-EPMC9710582 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature