From simple to complex: Reconstructing all-atom structures from coarse-grained models using cg2all
Yui Tik Pang1, Lixinhao Yang2, James C Gumbart3
1School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Structure (London, England : 1993)
|January 5, 2024
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.
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