2025Chen GMMs: Difference between revisions
Created page with "== Citation == M. Chen, “Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks,” Communications Biology, vol. 8, no. 1, pp. 1–9, 2025. == 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 w..." |
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Latest revision as of 10:37, 20 June 2025
Citation
M. Chen, “Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks,” Communications Biology, vol. 8, no. 1, pp. 1–9, 2025.
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 systemand have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex fromheterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.
Keywords
Links
https://www.nature.com/articles/s42003-025-08202-9