ArticlePLoS computational biology2010
Multiscale coarse-graining of the protein energy landscape.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed.
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Bottom-up Coarse-Grained Models of Asymmetric Membranes.The journal of physical chemistry. B · 2025Article
- Accurate prediction of thermoresponsive phase behavior of disordered proteins.Protein science : a publication of the Protein Society · 2025Article
- Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.Journal of chemical theory and computation · 2025Article
- Accurate prediction of thermoresponsive phase behavior of disordered proteins.bioRxiv : the preprint server for biology · 2025Article
- Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids.Journal of chemical theory and computation · 2024Article
- Machine learning coarse-grained potentials of protein thermodynamics.Nature communications · 2023Article
- Host Cell Membrane Capture by the SARS-CoV-2 Spike Protein Fusion Intermediate.ACS central science · 2023Article
- Machine learned coarse-grained protein force-fields: Are we there yet?Current opinion in structural biology · 2023Review
- Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy.Nature computational science · 2021Article
- Artificial Intelligence for Accelerating Time Integrations in Multiscale Modeling.Journal of computational physics · 2021Article
- Sampling of the conformational landscape of small proteins with Monte Carlo methods.Scientific reports · 2020Article
- Computational methods for exploring protein conformations.Biochemical Society transactions · 2020Review
- Molecular dynamics simulations in photosynthesis.Photosynthesis research · 2020Review
- Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning.Biomolecules · 2020Article
- Identification of native protein structures captured by principal interactions.BMC bioinformatics · 2019Article
- Computational Modeling of Realistic Cell Membranes.Chemical reviews · 2019Review
- MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing.Frontiers in molecular biosciences · 2019Article
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3 authors.
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Abstract
A variety of coarse-grained (CG) models exists for simulation of proteins. An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields. In the present work, atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence. The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains. CG energy landscapes computed from replica exchange simulations of the folding of Trpzip, Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state. The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding. The role of surface tension, backbone hydrogen bonding and the smooth pairwise CG landscape is discussed. Ab initio folding aside, the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip, Trp-cage, and the open to closed conformational transition of adenylate kinase, illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states.
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