ArticleNature communications2023
Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Systematic determination of disulfide bond reduction potentials reveals a nonequilibrium redox hierarchy in cyanobacteria.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multistep receptor binding of the hepatitis B virus preS1 domain.Nature communications · 2026Article
- Bridging Scales: Coarse-Grained Protein Models in Computational Biology.Advances in experimental medicine and biology · 2026Review
- Rapid estimation of protein folding pathways from sequence alone using AlphaFold2.Nature communications · 2025Article
- Navigating the landscape of protein folding and proteostasis: from molecular chaperones to therapeutic innovations.Signal transduction and targeted therapy · 2025Review
- Large-scale energy decomposition for the analysis of protein stability.Cell stress & chaperones · 2025Article
- Product release and substrate entry of aldehyde deformylating oxygenase revealed by molecular dynamics simulations.Biophysics and physicobiology · 2025Article
- A Stochastic Landscape Approach for Protein Folding State Classification.Journal of chemical theory and computation · 2024Article
- Assessing the functional roles of coevolving PHD finger residues.Protein science : a publication of the Protein Society · 2024Article
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Authors and funding
2 authors.
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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 protein folding. These predictions depict details of the folding processes beyond reproducing experimental results and provide a rationale for the folding mechanisms. Thus, our physics-based models enable accurate prediction of protein folding mechanisms with low computational complexity, paving the way for solving the folding process component of the 'protein folding problem'.
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