NYC Computational Cryo-EM Summer Workshop

Session

Poster Session and Reception

Aug 8, 2019, 5:15 PM

Ingrid Daubechies Auditorium (Flatiron Institute)

28. 3D Alignment based on Regionalized Deep Learning for Cryo-EM Reconstruction

Dr Amaya Jimenez-Moreno (National Center of Biotechnology (CSIC))

8/8/19, 5:15 PM

Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens. However, in a common Cryo-EM processing workflow, the 3D alignment step, due to the very low signal-to-noise ratio of Cryo-EM images, is a prone error process. Thus, the reconstructed 3D maps can show areas with low resolution.

In this work, a novel method to...

24. Computing and Understanding Statistical Models for Heterogeneous Biological Nano-machines

Yunye Gong (Cornell University), Prof. Peter C. Doerschuk (Cornell University)

8/8/19, 5:15 PM

The goal of this research is to understand the dynamical motion of nanoscale biological machines such as viruses directly from large sets of data. The ideal data would be 4-D measurements (3 spatial and 1 temporal) on each instance of the machine. The most informative available data are 2-D cryo-electron microscopy projection images of flash-frozen instances, one image for each instance....

47. Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps

Mrs Yifeng Fan

8/8/19, 5:15 PM

Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel approach called multi-frequency vector diffusion...

33. Deep learning and image analysis tools for automated analysis and assessment of cryo-EM datasets

Yilai Li (University of Michigan)

8/8/19, 5:15 PM

The growth of cryo-EM into a mainstream structural biology tool has led to its widespread adoption for users across a range of expertise, where experts represents a small fraction of cryo-EM users. Considering the manual and subjective decisions involved in solving a structure, such as the programs, parameters and determination of good micrographs and good 2D class averages, cryo-EM frustrates...

35. Deep Learning tools for particle picking

Dr David Maluenda (National Center of Biotechnology (CSIC))

8/8/19, 5:15 PM

Cryo-EM workflows require from tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Current methods for automatic particle-picking tend to suffer from high false-positive rates, hurdling the reconstruction process. Usually, the failures of one particle-picking algorithm are typically not the failures of another. Therefore, a smart...

30. DeepRes: A New Deep Learning-based Local Resolution Method

Dr Erney Ramírez-Aportela (CNB-CSIC (Madrid, Spain))

8/8/19, 5:15 PM

We present a method to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a local resolution-type of information. The algorithm (DeepRes) is based on deep learning 3D features detection. DeepRes is fully automatic and parameter free and avoids issues of most current methods, such as their insensitivity to enhancements due to B-factor sharpening (unless the 3D mask...

42. Fast rigid image alignment via factorization of the translation kernel

Prof. Aaditya Rangan (New York University)

8/8/19, 5:15 PM

An important subproblem in image analysis is the comparison of different images, which usually involves the calculation of inner products between pairs of images. In this poster we provide a new method for computing such inner products for arbitrary rotations and a user-specified range of translations. Our method takes advantage of the Fourier–Bessel basis to efficiently handle rotations,...

36. Manifold embedding analysis of TRPV1

Dr Sonya M. Hanson (Max Planck Institute of Biophysics)

8/8/19, 5:15 PM

The TRPV1 ion channel is a heat sensor that plays a key role in pain sensing pathways. Recent advances in cryo-electron microscopy (cryo-EM) have facilitated a recent explosion in the availability of TRP channel structures. Despite these structures, the temperature-sensing mechanism of any TRP channel remains poorly understood. The manifold embedding method in cryo-EM allows the identification...

32. ManifoldEM – Mapping Continuous Conformations, Energy Landscapes & Functional Pathways

Prof. Peter Schwander (University of Wisconsin Milwaukeee)

8/8/19, 5:15 PM

We have developed a manifold-based machine-learning approach for analyzing cryoEM single-particle data. This approach is capable of mapping continuous conformational changes of biological molecules along any user-selected  trajectory on the energy landscape, without timing information, supervision, or templates. Our unbiased approach (1) reveals the number of degrees of freedom exercised...

34. Multi-tapered CTF estimation

Ayelet Heimowitz (Princeton University)

8/8/19, 5:15 PM

We present a novel method for contrast transfer function (CTF) estimation. Our method is based on the multi-taper method for power spectral density estimation, which aims to reduce the bias and variance of the estimator. Furthermore, we use known properties of the CTF and of the background of the power spectrum to increase the accuracy of our estimation. We will show that the resulting...

22. New Methods for Denoising and CTF Correction

William Leeb (University of Minnesota)

8/8/19, 5:15 PM

We will present recent work on the problem of simultaneously denoising cryo-EM images and correcting for the effects of the contrast transfer function (CTF). The methods used are based on new results from high-dimensional principal component analysis and matrix recovery in the spiked covariance model. We use new spectral shrinkers that account for the effects of both the CTF and the colored...

29. Non-Uniformity of Projection Distributions Attenuates Resolution in Cryo-EM

Dr Philip Baldwin (The Salk Institute)

8/8/19, 5:15 PM

Virtually every single-particle cryo-EM experiment currently suffers from specimen adherence to the air-water interface, leading to a non-uniform distribution in the set of projection views. Non-uniform (anisotropic) distributions can negatively affect map quality, elongate structural features, and in some cases, prohibit interpretation altogether. Although some consequences of non-uniform...

25. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Tristan Bepler

8/8/19, 5:15 PM

Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, especially for unusually-shaped particles. To address these shortcomings, we develop Topaz, an efficient and...

46. Radial function based ab-initio tomographic reconstruction for cryo electron microscopy

Dr Étienne Baudrier

8/8/19, 5:15 PM

The scope of this paper is the tomographic reconstruction of the observed object in the ab-initio case where the volume has to be estimated only from a raw projection dataset. A new fast approach based on a parametric model of the volume is presented. The description of the model and the search of the parameters are detailed. The accuracy and robustness of the proposed reconstruction method is...

37. Reconstructing continuous distributions of 3D protein structure from cryo-EM images

Ellen Zhong (Massachusetts Institute of Technology)

8/8/19, 5:15 PM

In single particle cryo-EM, the central problem is to reconstruct the three-dimensional structure of a protein from $10^4-10^7$ noisy and randomly oriented two-dimensional projections. However, the imaged protein molecules may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of...

38. Spectral volumes for cryo-EM reconstruction with continuous heterogeneity

Amit Halevi (Princeton University), Dr Amit Moscovich (Princeton University)

8/8/19, 5:15 PM

In this work, we address the continuous heterogeneity problem. We parametrize the 3D density maps of the particles being imaged using a low-dimensional manifold of conformations. This parametrization is based on low-resolution reconstructions and Laplacian eigenmaps. We use this parametrization to form a generalized tomographic reconstruction problem which reconstructs a density map at each...

26. Z-contrast enhancement for small protein cryo-EM structure determination

James Chen

8/8/19, 5:15 PM

Single-particle EM 3D reconstruction comprises two aspects: data acquisition and data analysis. While “data analysis” aims to extract as much information as possible from noisy raw images, “data acquisition” strives to maximize the amount of information in data recording.

Electron scattering cross-sections of heavy atoms quantitatively differ from light atoms (namely C, N, O) abundant in...