WikiSysop: Created page with "== Citation == Chen, Muyuan / Toader, Bogdan / Lederman, Roy. Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mix..."

2023-08-24T06:04:05Z

Created page with "== Citation == Chen, Muyuan / Toader, Bogdan / Lederman, Roy. Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mix..."

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== Citation ==

Chen, Muyuan / Toader, Bogdan / Lederman, Roy. Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models. 2023.
J. Molecular Biology, Vol. 435, No. 9, p. 168014

== Abstract ==

Resolving the structural variability of proteins is often key to understanding the structure–function relationship
of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy
(CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within
the protein system from noisy micrographs. Here, we introduce an improved computational method that
uses Gaussian mixture models for protein structure representation and deep neural networks for conformation
space embedding. By integrating information from molecular models into the heterogeneity analysis,
we can analyze continuous protein conformational changes using structural information at the
frequency of 1/3 A^-1, and present the results in a more interpretable form.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S0022283623000700

== Related software ==

== Related methods ==

== Comments ==

2023Chen GMM - Revision history

WikiSysop: Created page with "== Citation == Chen, Muyuan / Toader, Bogdan / Lederman, Roy. Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mix..."