WikiSysop: Created page with "== Citation == Xu, S., Balanov, A., Singer, A. and Bendory, T. 2026. Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis. Biological Crystallography. 82, 4 (2026). == Abstract == Accurate orientation estimation is a crucial component of 3D molecular structure reconstruction, both in single-particle cryo-electron microscopy (cryo-EM) and in the increasingly popular field of cryo-electron tomography (cryo-E..."

2026-04-06T16:20:46Z

Created page with "== Citation == Xu, S., Balanov, A., Singer, A. and Bendory, T. 2026. Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis. Biological Crystallography. 82, 4 (2026). == Abstract == Accurate orientation estimation is a crucial component of 3D molecular structure reconstruction, both in single-particle cryo-electron microscopy (cryo-EM) and in the increasingly popular field of cryo-electron tomography (cryo-E..."

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== Citation ==

Xu, S., Balanov, A., Singer, A. and Bendory, T. 2026. Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis. Biological Crystallography. 82, 4 (2026).

== Abstract ==

Accurate orientation estimation is a crucial component of 3D molecular structure
reconstruction, both in single-particle cryo-electron microscopy (cryo-EM)
and in the increasingly popular field of cryo-electron tomography (cryo-ET).
The dominant approach, which involves searching for the orientation that
maximizes cross-correlation relative to given templates, is suboptimal, particularly
under low signal-to-noise conditions. In this work, we propose a Bayesian
framework for more accurate and flexible orientation estimation, with the
minimum mean-square error (MMSE) estimator serving as a key example.
Through simulations, we demonstrate that the MMSE estimator consistently
outperforms the cross-correlation-based method, especially in challenging low
signal-to-noise scenarios, and we provide a theoretical framework that supports
these improvements. When incorporated into iterative refinement algorithms
in the 3D reconstruction pipeline, the MMSE estimator markedly improves
reconstruction accuracy, reduces model bias and enhances robustness to the
‘Einstein from Noise’ artifact. Crucially, we demonstrate that orientationestimation
accuracy has a decisive effect on downstream structural heterogeneity
analysis. In particular, integrating the MMSE-based pose estimator into
frameworks for continuous heterogeneity recovery yields accuracy improvements
approaching those obtained with ground-truth poses, establishing
MMSE-based pose estimation as a key enabler of high-fidelity conformational
landscape reconstruction. These findings indicate that the proposed Bayesian
framework could substantially advance cryo-EM and cryo-ET by enhancing the
accuracy, robustness and reliability of 3D molecular structure reconstruction,
thereby facilitating deeper insights into complex biological systems.

== Keywords ==

== Links ==

https://journals.iucr.org/d/issues/2026/04/00/bar5003/index.html

== Related software ==

== Related methods ==

== Comments ==

2026Xu MMSE - Revision history