WikiSysop: Created page with "== Citation == Y. Li, Y. Zhou, J. Yuan, F. Ye, and Q. Gu, “CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction,” Nature Methods, pp. 1–9, 2024. == Abstract == Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorpor..."

2025-02-18T09:04:43Z

Created page with "== Citation == Y. Li, Y. Zhou, J. Yuan, F. Ye, and Q. Gu, “CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction,” Nature Methods, pp. 1–9, 2024. == Abstract == Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorpor..."

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== Citation ==

Y. Li, Y. Zhou, J. Yuan, F. Ye, and Q. Gu, “CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction,” Nature Methods, pp. 1–9, 2024.

== Abstract ==

Resolving conformational heterogeneity in cryogenic electron microscopy
datasets remains an important challenge in structural biology. Previous
methods have often been restricted to working exclusively on volumetric
densities, neglecting the potential of incorporating any preexisting
structural knowledge as prior or constraints. Here we present cryoSTAR,
which harnesses atomic model information as structural regularization
to elucidate such heterogeneity. Our method uniquely outputs both
coarse-grained models and density maps, showcasing the molecular
conformational changes at different levels. Validated against four diverse
experimental datasets, spanning large complexes, a membrane protein
and a small single-chain protein, our results consistently demonstrate
an efficient and effective solution to conformational heterogeneity with
minimal human bias. By integrating atomic model insights with cryogenic
electron microscopy data, cryoSTAR represents a meaningful step forward,
paving the way for a deeper understanding of dynamic biological processes.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41592-024-02486-1

== Related software ==

== Related methods ==

== Comments ==

2024Li CryoStar - Revision history