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.

2021/12/01 Muyuan Chen

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 (Dec 2nd)

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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. 

2021/11/10 Harshit Gupta

Gupta, Harshit

Harshit Gupta

Postdoc, Stanford Linear Accelerator Center (SLAC)

Website

Date and time: November 10th, 2021
Wednesday, at 8am PT / 11am ET / 4pm GMT / 5pm CET / 12am China (November 11th)

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CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM via Deep Adversarial Learning

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology over the last decade by delivering 3D structures of biomolecules at near-atomic resolution. It produces many noisy projections from separate instances of the same but randomly oriented biomolecule. These noisy projections are then used to reconstruct the 3D structure of the biomolecule. Scientists have spent the better part of the last 30 years designing a solid computational pipeline to achieve this goal. The result is an intricate multi-steps procedure that permits the regular discovery of new structures, but that is yet still prone to overfitting and irreproducibility. The most notable difficulties with the current paradigms are the need for pose-estimation methods, the reliance on user expertise for appropriate parameter tuning, and the non-straightforward extension to the handling of biomolecules with multiple conformations.

CryoGAN is a new paradigm for single-particle cryo-EM reconstruction based on unsupervised deep adversarial learning.  CryoGAN sidesteps the pose-estimation problem by using a generative adversarial network (GAN) to learn the 3D structure whose simulated projections most closely match the acquired projections in a distributional sense. The architecture of CryoGAN resembles that of standard GAN, with the twist that the generator network is replaced by a model of the cryo-EM image acquisition process. CryoGAN is an unsupervised algorithm that only demands projection images. No initial volume estimate or prior training is needed. CryoGAN requires minimal user interaction and can provide reconstructions in a matter of hours on a high-end GPU. In addition, it is backed by mathematical guarantees on the recovery of the correct structure. Moreover, its extension, called MultiCryoGAN can reconstruct continuous conformations of dynamic biomolecules, thus helping solve the most important open problem in the field without pose or conformation estimation.

2021/09/21 Grant Jensen

Jensen, Grant

Grant Jensen

Dean, College of Physical and Mathematical Sciences, Brigham Young University

Professor of Biology and Biophysics
California Institute of Technology

Date and time: September 29th, 2021
Wednesday, at 8am PT / 11am ET / 4pm BST / 5pm CEST / 11pm China

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Montage tomography of cryo-preserved specimens

Ariana Peck, Stephen D. Carter, Huanghao Mai, Songye Chen, Alister Burt, and Grant J. Jensen

Cryo-electron tomography reveals detailed views of macromolecules in situ, but the fields of view can be quite limited. Decades ago, montage tomography methods were developed to large areas of plastic sections, which are less radiation-sensitive than samples in vitreous ice. The dose sensitivity of vitreous samples has been considered prohibitive to montaging approaches, since portions of the sample must be exposed multiple times to allow image stitching. Taking advantage of several technical advances, we have now developed a montage data collection scheme that distributes the extra dose evenly throughout the specimen. We applied this method to image the thin edge of frozen-hydrated HeLa cells, and show that macromolecular details can be resolved across montage tomograms several microns across. Montage cryo-ET could be especially useful for imaging lamellae.

2021/06/16 Amit Singer

Amit Singer

Amit Singer

Professor of Mathematics at Princeton University

Wilson Statistics: Derivation, Generalization, and Applications to Cryo-EM

Date and time: June 16, 2021

Wednesday, at 8am PT / 11am ET / 3pm GMT / 5pm CET / 11pm China

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The power spectrum of proteins at high frequencies is remarkably well described by the flat Wilson statistics. Wilson statistics therefore plays a significant role in X-ray crystallography and more recently in cryo-EM. Specifically, modern computational methods for three-dimensional map sharpening and atomic modelling of macromolecules by single particle cryo-EM are based on Wilson statistics. In this talk we provide the first rigorous mathematical derivation of Wilson statistics. The derivation pinpoints the regime of validity of Wilson statistics in terms of the size of the macromolecule. Moreover, the analysis naturally leads to generalizations of the statistics to covariance and higher order spectra. These in turn provide theoretical foundation for assumptions underlying the widespread Bayesian inference framework for three-dimensional refinement and for explaining the limitations of autocorrelation based methods in cryo-EM.

2021/05/19 Lauren Ann Metskas

Lauren Ann Metskas

Lauren Ann Metskas

Postdoctoral Fellow at California Institute of Technology
Incoming Assistant Professor at Purdue University

Illuminating the Map: Current Practices, Challenges, and Future Applications of Cryo-CLEM

Date and time: May 19, 2021

Wednesday, at 8am PT / 11am ET / 3pm GMT / 5pm CET / 11pm China

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Correlated cryo-light and cryo-electron microscopy (cryo-CLEM) has become an increasingly popular method for combining the resolving power of cryo-EM with the specificity of fluorescence. Although cryo-fluorescence microscopy suffers from optical limitations, it is a powerful way to target the resolving power of cryo-EM toward proteins of interest in heterogeneous cellular environments. Two large areas of methods developments are ongoing in both hardware and software: improving the correlation precision and accuracy to facilitate single-protein targeting, and expanding the cryo-fluorescence toolkit to include room-temperature approaches capable of targeting functions or specific protein conformations. This webinar will begin with a theoretical overview of current obstacles and considerations for software developers seeking to improve correlation precision and accuracy, and conclude with our recent efforts to improve cryo-fluorescence data quality, including characterizing the effect of cryo temperatures on fluorophores and establishing function-based localization in cryo-CLEM.

2021/05/05 Dong Si

Dong Si

Dong Si

Assistant Professor at University of Washington Bothell

Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM

Date and time: May 5, 2021

Wednesday, at 8am PT / 11am ET / 3pm GMT / 5pm CET / 11pm China

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Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, I will talk about the representative ML/DL-based de novo cryo-EM modeling methods that we developed.

2021/04/07 David DeRosier

David DeRosier

David DeRosier

Professor Emeritus of Biology at Brandeis University

Where in the cell is my protein: revisited.

Date and time: April 7, 2021

Wednesday, at 8am PT / 11am ET / 3pm GMT / 5pm CET / 11pm China

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Cryo electron tomograms (cryo-ET) are nothing short of amazing. There is so much to see, but wouldn’t it be nice if we could identify all those structural features in terms of their molecular identity? The combination of cryo-single molecule localization microscopy (cryo-SMLM) and cryo-ET should provide a pathway to making such identification. What are the challenges to realizing the combination’s full potential? Why the fish tank?

2021/03/24 Slavica Jonic

Slavica Jonic

Slavica Jonic

CNRS Research Director

Combining normal mode analysis, image analysis, and deep learning for in vitro and in situ studies of continuous conformational variability of macromolecular complexes

Date and time: March 24th, 2021
Wednesday, at 8am PT / 11am ET / 3pm GMT / 4pm CET / 11pm China

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HEMNMA is a method to analyze continuous conformational variability of purified macromolecular complexes (in vitro), introduced in 2014 (Jin et al., Structure 22:496-506, 2014). Its software with user friendly graphical interface is part of the open-source ContinuousFlex plugin of Scipion 2 and Scipion 3 (Harastani et al., Protein Science 29:223–236, 2020). HEMNMA combines single particle image analysis, normal mode analysis, and dimension reduction techniques to visualize the full distribution of states (conformational landscape) in a low-dimensional space (usually 2D or 3D space), from which one can obtain 3D reconstructions and movies of molecular motions along desired directions. After a brief reminder on HEMNMA, I will present current developments, including combining HEMNMA with deep learning to accelerate the conformational landscape determination and an extension of HEMNMA to analyze continuous conformational variability of macromolecular complexes in cells from in situ cryo electron tomography data.