ArticleProceedings of the National Academy of Sciences of the United States of America2012
De novo prediction of protein folding pathways and structure using the principle of sequential stabilization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- DreamFold: A World Model to efficiently generate protein folding pathways in the latent space.Protein science : a publication of the Protein Society · 2026Article
- Recent Advances in Protein Folding Pathway Prediction through Computational Methods.Current medicinal chemistry · 2024Review
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.Bioinformatics advances · 2023Article
- Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.Scientific reports · 2021Article
- Water as a Good Solvent for Unfolded Proteins: Folding and Collapse are Fundamentally Different.Journal of molecular biology · 2020Review
- Review
- Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico.BMC bioinformatics · 2019Article
- Gene-specific features enhance interpretation of mutational impact on acid α-glucosidase enzyme activity.Human mutation · 2019Article
- Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields.International journal of molecular sciences · 2019Review
- Accurate Protein-Folding Transition-Path Statistics from a Simple Free-Energy Landscape.The journal of physical chemistry. B · 2018Article
- Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours.PLoS computational biology · 2018Article
- A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations.Biophysical journal · 2018Article
- The case for defined protein folding pathways.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Cooperative folding near the downhill limit determined with amino acid resolution by hydrogen exchange.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Network representation of conformational transitions between hidden intermediates of Rd-apocytochrome b562.The Journal of chemical physics · 2015Article
- Sequence, structure, and cooperativity in folding of elementary protein structural motifs.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Even with nonnative interactions, the updated folding transition states of the homologs Proteins G & L are extensive and similar.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Native contact density and nonnative hydrophobic effects in the folding of bacterial immunity proteins.PLoS computational biology · 2015Article
- Knotted proteins: A tangled tale of Structural Biology.Computational and structural biotechnology journal · 2015Review
- General mechanism of two-state protein folding kinetics.Journal of the American Chemical Society · 2014Article
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Authors and funding
3 authors.
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Abstract
Motivated by the relationship between the folding mechanism and the native structure, we develop a unified approach for predicting folding pathways and tertiary structure using only the primary sequence as input. Simulations begin from a realistic unfolded state devoid of secondary structure and use a chain representation lacking explicit side chains, rendering the simulations many orders of magnitude faster than molecular dynamics simulations. The multiple round nature of the algorithm mimics the authentic folding process and tests the effectiveness of sequential stabilization (SS) as a search strategy wherein 2° structural elements add onto existing structures in a process of progressive learning and stabilization of structure found in prior rounds of folding. Because no a priori knowledge is used, we can identify kinetically significant non-native interactions and intermediates, sometimes generated by only two mutations, while the evolution of contact matrices is often consistent with experiments. Moreover, structure prediction improves substantially by incorporating information from prior rounds. The success of our simple, homology-free approach affirms the validity of our description of the primary determinants of folding pathways and structure, and the effectiveness of SS as a search strategy.
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