Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

ABSTRACT: Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail-an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This "neural upscaling" procedure exp

SUBMITTER: Duong VT 

PROVIDER: S-EPMC8698800 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature