ArticlePLoS computational biology2023
Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Variable Resolution Maps (VRM) in CCTBX and Phenix: Accounting For Local Resolution In cryoEM.bioRxiv : the preprint server for biology · 2026Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- An Empirical Biasing Force Constant to Minimize Overfitting in Cryo-EM Flexible Fitting Refinement.Journal of chemical information and modeling · 2025Article
- Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics.PLoS computational biology · 2025Article
- Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations.Nature communications · 2025Article
- Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.Scientific reports · 2024Article
- Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.PLoS computational biology · 2024Article
- Automated simulation-based membrane protein refinement into cryo-EM data.Biophysical journal · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential-based on relative entropy-in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit.
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