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

Chen, M.

doi:10.1101/2024.09.27.615511

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bioRxiv · 10.1101/2024.09.27.615511

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

Chen, M.

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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.

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Chen, M.. 2024-09-27. Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks. https://doi.org/10.1101/2024.09.27.615511

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