ArticleCommunications biology2025
Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks.
Article in Communications biology, 2025. 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.
- Article
- Structural dynamics of kappa opioid receptor interactions with β-arrestin 1.Nature communications · 2026Article
- CryoJAX: a cryo-electron microscopy image-simulation library in JAX.Acta crystallographica. Section D, Structural biology · 2026Article
- Diverse bacterial pattern recognition receptors sense the conserved phage proteome.bioRxiv : the preprint server for biology · 2026Article
- Comparing cryo-EM methods and molecular dynamics simulation to investigate heterogeneity in ligand-bound TRPV1.Nature communications · 2025Article
- Human O-GlcNAcase catalytic-stalk dimer anchors flexible histone binding domains.Communications chemistry · 2025Article
- Cryo-electron microscopy ensemble optimization using individual particles and physical constraints.bioRxiv : the preprint server for biology · 2025Article
- cryoJAX: A Cryo-electron Microscopy Image Simulation Library In JAX.bioRxiv : the preprint server for biology · 2025Article
- Structural dynamics of mitochondrial ATP synthase inbioRxiv : the preprint server for biology · 2025Article
- HumanResearch square · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
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Abstract
Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.
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