Neural Upscaling from Residue-level Protein Structure Networks to Atomistic Structure

TL;DR

This paper presents a machine learning approach to reconstruct atomistic protein structures from topological graph representations, enabling detailed structural insights from coarse-grained models.

Contribution

It introduces a neural upscaling method that combines machine learning with physical constraints to infer atomic coordinates from protein structure networks.

Findings

Successfully recovers transient secondary structures in disordered proteins

Effectively recapitulates detailed atomic features from PSNs

Demonstrates potential for scalable, atomistic-level protein modeling

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 structure, 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 exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 $\mu$s atomistic molecular dynamics trajectory of A$\beta_{1-40}$, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.