WikiSysop: Created page with "== Citation == C. T. Van et al., “Probabilistic single-particle cryo-EM ab initio 3D reconstruction in SIMPLE,” Biological Crystallography, vol. 81, no. 8, 2025. == Abstract == Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires ab initio 3D reconstruction of density volume(s) from 2D images (particles). This large-scale inverse problem requires the determination of many million degrees o..."

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Created page with "== Citation == C. T. Van et al., “Probabilistic single-particle cryo-EM ab initio 3D reconstruction in SIMPLE,” Biological Crystallography, vol. 81, no. 8, 2025. == Abstract == Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires ab initio 3D reconstruction of density volume(s) from 2D images (particles). This large-scale inverse problem requires the determination of many million degrees o..."

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== Citation ==

C. T. Van et al., “Probabilistic single-particle cryo-EM ab initio 3D reconstruction in SIMPLE,” Biological Crystallography, vol. 81, no. 8, 2025.

== Abstract ==

Three-dimensional (3D) structure determination by single-particle analysis of
cryo-electron microscopy (cryo-EM) images requires ab initio 3D reconstruction
of density volume(s) from 2D images (particles). This large-scale inverse
problem requires the determination of many million degrees of freedom from
extremely noisy experimental measurements. Here, we introduce a new
approach to probabilistic multi-volume ab initio 3D reconstruction for simultaneous
estimation of the relative particle 3D orientations and partitioning of
the particles into groups with distinct structural states. To account for further
structural variability within the discrete state groups, due to for example
regional disorder, flexibility or partial occupancy of associating ligands, we
introduce a new method for adaptive non-uniform regularization based on
iterated conditional modes (ICMs). Our ICM regularization approach can be
viewed as a spatially varying real-space prior that optimizes the connectivity of
the reconstructed density map(s). Our method is designed to run in real time as
the microscope collects the data, which puts significant constraints on algorithm
scalability and flexibility with regard to how new particles are incorporated. We
describe the probabilistic optimization and non-uniform regularization theory in
detail. Finally, we provide numerous benchmarking examples, both on publicly
available standard test data sets and on data sets acquired at our cryo-EM
facility at the National Cancer Institute, National Institutes of Health. The
implementation of our new multi-volume ab initio 3D reconstruction approach
is part of the SIMPLE software suite, which is provided open source at https://
github.com/hael/SIMPLE.

== Keywords ==

== Links ==

https://journals.iucr.org/d/issues/2025/08/00/ic5124/index.html

== Related software ==

== Related methods ==

== Comments ==

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