One bead per residue can describe all-atom protein structures.

ABSTRACT: Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical ap

SUBMITTER: Heo L 

PROVIDER: S-EPMC10872525 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature