ArticleIsrael journal of chemistry2014
Learning To Fold Proteins Using Energy Landscape Theory.
Article in Israel journal of chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Article
- FINCHES: A Computational Framework for Predicting Intermolecular Interactions in Intrinsically Disordered Proteins.International journal of molecular sciences · 2025Review
- Generating the polymorph landscapes of amyloid fibrils using AI: RibbonFold.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning of pair-contact process with diffusion.Scientific reports · 2022Article
- Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Frustration Dynamics and Electron-Transfer Reorganization Energies in Wild-Type and Mutant Azurins.Journal of the American Chemical Society · 2022Article
- Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process?International journal of molecular sciences · 2022Review
- Resolving the fine structure in the energy landscapes of repeat proteins.QRB discovery · 2022Article
- Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire.Nature computational science · 2021Article
- OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations.PLoS computational biology · 2021Article
- Surveying biomolecular frustration at atomic resolution.Nature communications · 2020Article
- Article
- AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes.Nucleic acids research · 2020Article
- Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins.Journal of chemical theory and computation · 2020Article
- The acidic tail of HMGB1 regulates its secondary structure and conformational flexibility: A circular dichroism and molecular dynamics simulation study.Computational and structural biotechnology journal · 2020Article
- Disorder Mediated Oligomerization ofThe journal of physical chemistry. B · 2019Article
- Competition of individual domain folding with inter-domain interaction in WW domain engineered repeat proteins.Physical chemistry chemical physics : PCCP · 2019Article
- Forging tools for refining predicted protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Structural and Dynamical Order of a Disordered Protein: Molecular Insights into Conformational Switching of PAGE4 at the Systems Level.Biomolecules · 2019Article
- AWSEM-IDP: A Coarse-Grained Force Field for Intrinsically Disordered Proteins.The journal of physical chemistry. B · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.