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.

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

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

Next Talk

Schwander. Peter

Peter Schwander

PhD, Assoc. Professor

Date and time: June 8th, 2022
Wednesday, at 8am PT / 11am ET / 4pm BST / 5pm CEST / 11pm China

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

Routes to Extract Biological Function from single-particle cryoEM 

Recent progress in data analysis of single-particle cryogenic Electron Microscopy (cryoEM); such as Geometric Machine Learning algorithms, allows one to retrieve the conformational spectrum of heterogeneous molecular ensembles.  Until recently, most efforts have been attempted under equilibrium conditions [1,2], where the conformational spectrum is time-independent, and thus directly yields the free-energy landscape. However, most biological systems operate far from equilibrium to sustain the processes that constitute life.

Under nonequilibrium conditions, the functional pathways are time-dependent, and the evolution of the conformational spectrum can be described by a Fokker-Planck equation, however with an unknown operator. State-of-the-art developments in Machine Learning, the so-called Physics-Informed Neural Networks (PINN) [3], allows one to retrieve the underlying Fokker-Planck operator from sparse observations alone [4], providing a complete physics-based description of the nonequilibrium process. Moreover, time-resolved cryoEM experiments have recently become practical [5]. Together, this enables us to combine the advantages of time-resolved serial crystallography (‘nonequilibrium processes’) with the advantages of single-particle methods (‘avoids averaging over unlike particles’).

Based on these opportunities, we present a conceptional and algorithmic framework to extract functional pathways from nonequilibrium from a collection of time-resolved single-particle images. This constitutes an unexplored route in studying biological function and structural dynamics under nonequilibrium conditions.


1. Dashti, A. et al. Trajectories of the ribosome as a Brownian nanomachine. Proc. Natl. Acad.  Sci. U. S. A. 111, 17492–7 (2014).

2. Dashti, A. et al. Retrieving functional pathways of biomolecules from single-particle snapshots. Nat. Commun. 11, 4734 (2020).

3. Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, (2019).

4. Chen, X., Yang, L., Duan, J. & Karniadakis, G. E. Solving inverse stochastic problems from discrete particle observations using the Fokker-Planck equation and physics-informed neural networks. SIAM Journal on Scientific Computing 43, (2021).

5. Dandey, V. et al. Time-resolved cryo-EM using Spotiton, Nature Meth., 17, 897 (2020).

Last Talk

Scheres, Sjors

Sjors Scheres

MRC Laboratory of Molecular Biology

Date and time: March 23rd, 2022
Wednesday, at 8am PT / 11am ET / 3pm GMT / 4pm CET / 11pm China

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

High-throughput cryo-EM structure determination of amyloids

The formation of amyloid filaments is characteristic of various degenerative diseases. Recent breakthroughs in electron cryo-microscopy (cryo-EM) have led to atomic structure determination of multiple amyloid filaments, both of filaments assembled in vitro from recombinant proteins, and of filaments extracted from diseased tissue. These observations revealed that a single protein may adopt multiple different amyloid folds, and that in vitro assembly does not necessarily lead to the same filaments as those observed in disease. In order to develop relevant model systems for disease, and ultimately to better understand the molecular mechanisms of disease, it will be important to determine which factors determine the formation of distinct amyloid folds. High-throughput cryo-EM methods will facilitate the screening of large numbers of in vitro assembly conditions. To this end, I will describe a new filament picking algorithm based on the Topaz approach, and outline image processing strategies in Relion that enable atomic structure determination of amyloids within days.

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