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


All TalksPast talksUpcoming Talks


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 gather.town 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.