ArticleNature methods2024
CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Improving Conformational Ensembles of Folded Proteins in Go̅Martini.Journal of chemical theory and computation · 2026Article
- CryoJAX: a cryo-electron microscopy image-simulation library in JAX.Acta crystallographica. Section D, Structural biology · 2026Article
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Cryo-electron microscopy ensemble optimization using individual particles and physical constraints.bioRxiv : the preprint server for biology · 2025Article
- The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.bioRxiv : the preprint server for biology · 2025Article
- cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX.bioRxiv : the preprint server for biology · 2025Article
- Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.Acta crystallographica. Section D, Structural biology · 2025Article
- Article
- InstaMap: instant-NGP for cryo-EM density maps.Acta crystallographica. Section D, Structural biology · 2025Article
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
- AI-based methods for biomolecular structure modeling for Cryo-EM.Current opinion in structural biology · 2025Review
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Authors and funding
5 authors.
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Abstract
Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorporating any preexisting structural knowledge as prior or constraints. Here we present cryoSTAR, which harnesses atomic model information as structural regularization to elucidate such heterogeneity. Our method uniquely outputs both coarse-grained models and density maps, showcasing the molecular conformational changes at different levels. Validated against four diverse experimental datasets, spanning large complexes, a membrane protein and a small single-chain protein, our results consistently demonstrate an efficient and effective solution to conformational heterogeneity with minimal human bias. By integrating atomic model insights with cryogenic electron microscopy data, cryoSTAR represents a meaningful step forward, paving the way for a deeper understanding of dynamic biological processes.
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