From simple to complex: Reconstructing all-atom structures from coarse-grained models using cg2all

Yui Tik Pang; Lixinhao Yang; James C Gumbart

doi:10.1016/j.str.2023.12.004

From simple to complex: Reconstructing all-atom structures from coarse-grained models using cg2all

Structure. 2024 Jan 4;32(1):5-7. doi: 10.1016/j.str.2023.12.004.

Authors

Yui Tik Pang¹, Lixinhao Yang², James C Gumbart³

Affiliations

¹ School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
² School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA.
³ School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA. Electronic address: gumbart@physics.gatech.edu.

PMID: 38181727
DOI: 10.1016/j.str.2023.12.004

Abstract

In this issue of Structure, Heo and Feig present cg2all, a novel deep-learning model capable of efficiently predicting all-atom protein structures from coarse-grained (CG) representations. The model maintains high accuracy, even when the CG model is simplified to a single bead per residue, and has a number of promising applications.

Grants and funding

R01-GM148586)/NH/NIH HHS/United States