ArticleThe journal of physical chemistry. B2016
Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model.
Article in The journal of physical chemistry. B, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- FINCHES: A Computational Framework for Predicting Intermolecular Interactions in Intrinsically Disordered Proteins.International journal of molecular sciences · 2025Review
- Molecular Dynamics Simulations in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Illuminating Intrinsically Disordered Proteins with Integrative Structural Biology.Biomolecules · 2023Review
- Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble.Scientific reports · 2022Article
- From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules.Frontiers in molecular biosciences · 2021Review
- A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules.The European physical journal. B · 2021Article
- Article
- Disorder Mediated Oligomerization ofThe journal of physical chemistry. B · 2019Article
- Structural and Dynamical Order of a Disordered Protein: Molecular Insights into Conformational Switching of PAGE4 at the Systems Level.Biomolecules · 2019Article
- Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins.Computational and structural biotechnology journal · 2019Review
- AWSEM-IDP: A Coarse-Grained Force Field for Intrinsically Disordered Proteins.The journal of physical chemistry. B · 2018Article
- The Role of Hydrogen Bonding in the Folding/Unfolding Process of Hydrated Lysozyme: A Review of Recent NMR and FTIR Results.International journal of molecular sciences · 2018Review
- Template-Guided Protein Structure Prediction and Refinement Using Optimized Folding Landscape Force Fields.Journal of chemical theory and computation · 2018Article
- PAGE4 and Conformational Switching: Insights from Molecular Dynamics Simulations and Implications for Prostate Cancer.Journal of molecular biology · 2018Article
- Protein structure prediction: making AWSEM AWSEM-ER by adding evolutionary restraints.Proteins · 2017Article
- Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis: Analyzing Variation at the Protein Level.Global heart · 2017Review
- Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington's disease.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins.The journal of physical chemistry. B · 2017Article
- The Aggregation Free Energy Landscapes of Polyglutamine Repeats.Journal of the American Chemical Society · 2016Article
- Exploring the aggregation free energy landscape of the amyloid-β protein (1-40).Proceedings of the National Academy of Sciences of the United States of America · 2016Article
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5 authors.
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Abstract
The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six α-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.
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