Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models.

ABSTRACT: Recent breakthroughs in highly accurate protein structure prediction using deep neural networks have made considerable progress in solving the structure prediction component of the 'protein folding problem'. However, predicting detailed mechanisms of how proteins fold into specific native structures remains challenging, especially for multidomain proteins constituting most of the proteomes. Here, we develop a simple structure-based statistical mechanical model that introduces nonlocal interactions driving the folding of multidomain proteins. Our model successfully predicts protein folding processes consistent with experiments, without the limitations of protein size and shape. Furthermore, slight modifications of the model allow prediction of disulfide-oxidative and disulfide-intact protei

SUBMITTER: Ooka K 

PROVIDER: S-EPMC10587348 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature