Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks

Chen, Muyuan

doi:10.1038/s42003-025-08202-9

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks

Journal Article · Sun May 25 00:00:00 EDT 2025 · Communications Biology

DOI:https://doi.org/10.1038/s42003-025-08202-9· OSTI ID:2567049

^[1]

SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford Univ., CA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2567049

Journal Information:: Communications Biology, Journal Name: Communications Biology Journal Issue: 1 Vol. 8; ISSN 2399-3642

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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