The One World Cryo-EM seminar series is a platform for discussion of algorithms, computational methods and mathematical problems in cryo-EM.

Online, Once a Month. Wednesday at 8am PT / 11am ET/ 4pm BST / 5pm CET / 11pm China.

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Organizers: Joakim Andén, Dorit Hanein, Roy R. Lederman and Steven J. Ludtke

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Next Talk

Aaditya Rangan
Associate Professor, Courant Institute, New York University and Flatiron Institute.

Date and time: May 8th, 2024 Wednesday, at 8am PDT / 11am EDT / 3pm BST / 5pm CEST / 11pm China

Ab-initio Reconstruction from small datasets: using entropy-maximization and principal modes (EMPM).

In this talk I’ll describe a simple algorithm for obtaining a low-resolution model from a small number (~1000) picked-particle images. The algorithm (termed EMPM) is similar to classical alternating-minimization, but with a slight twist: the entropy of the viewing-angle distribution is maximized during the search. This entropy-maximization greatly improves robustness and accuracy, allowing EMPM to outperform many existing ab-initio reconstruction algorithms. Because of its low computational overhead, this algorithm could facilitate statistical strategies such as bootstrapping and cross-validation, along with the rapid assessment of particle quality.

Last Talk

Yuntao Liu

Postdoctoral scholar, California NanoSystems Institute, UCLA

Date and time: Apr 10th, 2024
Wednesday, at 8am PST / 11am EDT / 3pm GMT / 5pm CET / 11pm China

For the zoom and links, please join the One World Cryo-EM mailing list.

Single-particle IsoNet: an AI-based method to overcome the “preferred” orientation problem

While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called “preferred” orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet’s capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, β-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred orientation problem.