2023/05/03 Amit Moscovich

Amit Moscovich

Assistant Professor, Department of Statistics and Operations Research, Tel Aviv University

Date and time: May 3rd, 2023
Wednesday, at 8am PDT / 11am EDT / 4pm BST / 5pm CEST / 11pm China

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Tools for heterogeneity in cryo-EM: manifold learning, disentanglement and optimal transport

Despite much recent progress, the reconstruction and analysis of macromolecules with continuous conformational heterogeneity remains a key challenge.  In this talk we present two promising tools that can aid in various stages of the reconstruction and analysis pipelines.

In the first part of the talk, the notion of manifold factorization and disentanglement is presented.  This is an approach to dimensionality reduction where different aspects of the data are assigned separate coordinates.  It has potential applications both for the reconstruction of single-particle cryo-EM samples with continuous heterogeneity and for the analysis of the reconstructed volumetric datasets.

In the second part of the talk, we will discuss the potential applications of optimal transport for cryo-EM, in particular for the analysis of heterogeneous samples, as well as for class-averaging and particle picking.  Optimal transport metrics are closely related to physical motion, making them a natural choice for many of the core problems in cryo-EM.  Historically, computational bottlenecks have limited the applicability of optimal transport.  However, recent advances in computational optimal transport have yielded fast approximation schemes that can be readily used for the analysis of high-resolution images and volumetric arrays.

2023/01/18 Carlos Oscar Sorzano

Carlos Oscar Sorzano

Staff Researcher at the National Center of Biotechnology (CSIC)

Date and time: January 18th, 2023
Wednesday, at 8am PST / 11am EST / 4pm GMT / 5pm CET / midnight China

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Bias, variance and map validation in Single Particle Analysis by CryoEM

Single Particle Analysis by CryoEM has established as a mature technique to elucidate the three-dimensional structure of biological macromolecules. Once the experimental data is acquired, reconstructing the macromolecule’s map involves the estimation of millions of parameters. Due to the low Signal-to-Noise Ratio, this estimation is prone to mistakes. Depending on the error size, these mistakes may result into small perturbations in the reconstructed map (variance) or large artifacts (bias). In this talk we will discuss different kinds of mistakes that can be committed and how to identify and tackle them.

2023/03/01 Petar Petrov

Petar Petrov

Postdoctoral Scholar at University of California, Berkeley

Date and time: March 1st, 2023
Wednesday, at 8am PST / 11am EST / 4pm GMT / 5pm CET / midnight China

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Laser phase-contrast Cryo-EM and associated computational opportunities

A phase plate can provide optimum image contrast for weak-phase objects in transmission  electron microscopy, but approaches toward realizing a phase plate have suffered from  instabilities. We have developed a phase plate that is based on coherently phase-shifting the  electron wave function by a laser beam, which is built up to a record-high intensity of ~400  GW/cm^2 by resonance in a Fabry-Perot cavity. We have demonstrated contrast enhancement  with the laser phase plate (LPP) and shown the long-term stability of the device, as well as  generated a high-resolution map of 20S proteasome particles using a standard single-particle  cryo-electron microscopy (Cryo-EM) workflow. 

This talk will focus on our recent work to move beyond proof-of-concept, as well as the  computational opportunities that lie ahead. To demonstrate the benefits of the LPP to Cryo-EM  as well as cryo-electron tomography, we will soon begin working with a state-of-the-art  microscope equipped with a spherical aberration corrector, gun monochromator, and post column energy filter. We will explore improvements to the phase plate design and pursue new  strategies for image acquisition and processing, such as high-resolution two-dimensional  template matching.

2023/02/01 Tamir Bendory

Tamir Bendory

Electrical Engineering Assistant Professor at Tel Aviv University

Date and time: February 1st, 2023
Wednesday, at 8am PST / 11am EST / 4pm GMT / 5pm CET / midnight China

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Recovering small molecular structures using Cryo-EM

Any current Cryo-EM algorithmic pipeline entails recovering the 3-D structure after particle picking. However, the signal-to-noise ratio of the data, and thus the reliability of particle picking, drops with the molecular mass of the specimens. Accordingly, it is commonly believed that Cryo-EM cannot be used to map molecules with a molecular mass below a certain threshold. Challenging this misconception, I will argue that finding the particle picking is not a prerequisite for structure determination and thus small molecules are, at least in principle, within reach of Cryo-EM. Then, I will introduce two computational frameworks to bypass particle picking and show numerical results.

2022/11/09 Willem Diepeveen

Willem Diepeveen

Doctoral Candidate at Cambridge University

Date and time: November 9nd, 2022
Wednesday, at 8am PST / 11am EST / 4pm GMT / 5pm CET / midnight China

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Revisiting Orientation Estimation in Cryo-EM Volume Refinement

In Cryo-EM 3D map refinement, popular software packages jointly reconstruct the 3D map while estimating orientations. The orientation estimation can roughly be categorised in two type of approaches: marginalisation and optimisation. While the former tends to be more robust to noise, the latter has better consistency with respect to the data. So far it appears difficult to obtain both data-consistency and noise-robustness in a single method.

In this talk we will revisit the orientation estimation process. In particular, we develop an alternative that can be interpreted as being “in between marginalisation and optimisation” and argue that this new method is both robust to noise and data-consistent. Additionally, the framework of lifting-based global optimisation on manifolds allows analysis of the proposed methods that lead to several practical theoretical guarantees. Both theoretical results and performance on simulated data will be tested in numerical experiments.

2022/10/05 Daisuke Kihara

Daisuke Kihara

Professor of Biological Sciences, Computer Science, Purdue University

Date and time: September 7th, 2022
Wednesday, at 8am PT / 11am ET / 4pm BST / 5pm CEST / 11pm China

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Validating and Building Protein Structure Models for cryo-EM Maps Using Deep Learning

Cryo-electron microscopy (cryo-EM) has become one of the main experimental methods for determining protein structures. Protein structure modeling from cryo-EM is in general more difficult than X-ray crystallography since the resolution of maps is often not high enough to specify atom positions. We have been developing a series of computational methods for modeling protein structures from cryo-EM maps. For maps at medium resolution, deep learning can provide useful structure information for structure validation and modeling. Particularly, we have recently developed a protein model quality assessment score, DAQ, which compares local density patterns captured by deep learning with amino acid positions in a model, and detect potential errors in the model. In a large-scale analysis of protein models from cryo-EM, we found that not a small number of models may have some errors. All the tools we developed are available at https://kiharalab.org/emsuites/.

2022/09/07 Frank DiMaio

DiMiaio, Frank

Frank DiMaio

Associate Professor, Institute for Protein Design, University of Washington

Date and time: September 7th, 2022
Wednesday, at 8am PT / 11am ET / 4pm BST / 5pm CEST / 11pm China

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Machine learning structure prediction and cryoEM map interpretation

The machine-learning structure prediction tools AlphaFold and RoseTTAfold have dramatically simplified low-resolution cryoEM map interpretation.  Following this, we describe three tools our lab has developed in conjunction with these approaches.  First, we describe novel machine-learning methodology for detecting errors in models built against low-resolution cryoEM density.  Next, we describe tools for modelling ligands into low-resolution cryoEM density.  Finally, we describe our efforts at adding to RoseTTAFold the ability to predict the structure of protein/nucleic-acid complexes.

2022/06/08 Peter Schwander

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

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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).

2022/03/23 Sjors Scheres

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

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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.

2022/01/26 Jonas Adler

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)

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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.