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

Online, Every Other Wednesday, at 8am PT / 11am ET/ 4pm BST / 5pm CET / 11pm China.

Registration has changed. For zoom links and announcements, please register to our mailing list. This is a read-only mailing list (users cannot post). Please use your institution/ company email – we approve registrations manually and this helps us know who you are . You may receive an email asking you to confirm your registration, it is important that you click the link, otherwise we will not see your request.

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

All TalksPast talksUpcoming Talks

One World Cryo-EM will be back in September 2021.

Next Talk

Adler, Jonas

Jonas Adler

Senior Research Scientist at DeepMind

Date and time: January 26th, 2022
Wednesday, at 8am PT / 11am ET / 4pm GMT / 5pm CET / 12am China (Jan 27th)

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

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs

Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination. Despite advances in high resolution reconstruction, a majority of cryo-EM experiments provide either a single state of the studied macromolecule, or a relatively small number of its conformations. This reduces the effectiveness of the technique for proteins with flexible regions, which are known to play a key role in protein function. Recent methods for capturing conformational heterogeneity in cryo-EM data model it in volume space, making recovery of continuous atomic structures challenging. Here we present a fully deep-learning-based approach using variational auto-encoders (VAEs) to recover a continuous distribution of atomic protein structures and poses directly from picked particle images and demonstrate its efficacy on realistic simulated data. We hope that methods built on this work will allow incorporation of stronger prior information about protein structure and enable better understanding of non-rigid protein structures.

Last Talk

Chen, Muyuan

Muyuan Chen

Instructor, Baylor College of Medicine

Date and time: December 1st, 2021
Wednesday, at 8am PT / 11am ET / 4pm GMT / 5pm CET / 12am China (November 11th)

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

Deep learning based Gaussian mixture model for characterizing variability in CryoEM

Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. CryoEM provides direct visualization of individual macromolecules sampling different conformational and compositional states, but characterization of continuous conformational changes or large numbers of discrete states without human supervision remains challenging. Here, we present a machine learning algorithm to determine a conformational landscape for proteins or complexes using a 3-D Gaussian mixture model mapped onto 2-D particle images in known orientations. Using a deep neural network architecture, the method can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. The algorithm is applied to multiple biological systems, to explore compositional and conformational changes at a range of scales. 

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