Mapping Neutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain by Structure-Guided High-Resolution Serology.

N-terminal domain antigenic mapping reveals a site of vulnerability for SARS-CoV-2.

Efficient therapeutic options are needed to control the spread of SARS-CoV-2 that has caused more than 922,000 fatalities as of September 13th, 2020. We report the isolation and characterization of two ultrapotent SARS-CoV-2 human neutralizing antibodies (S2E12 and S2M11) that protect hamsters against SARS-CoV-2 challenge. Cryo-electron microscopy structures show that S2E12 and S2M11 competitively block ACE2 attachment and that S2M11 also locks the spike in a closed conformation by recognition of a quaternary epitope spanning two adjacent receptor-binding domains. Cocktails including S2M11, S2E12 or the previously identified S309 antibody broadly neutralize a panel of circulating SARS-CoV-2 isolates and activate effector functions. Our results pave the way to implement antibody cocktails for prophylaxis or therapy, circumventing or limiting the emergence of viral escape mutants.

/pdf/ultrapotent-human-antibodies-protect-against-sars-cov-2-1mwpzowdvz.pdf

Ultrapotent human antibodies protect against SARS-CoV-2 challenge via multiple mechanisms

Targeting the interaction between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor is a promising therapeutic strategy. We designed inhibitors using two de novo design approaches. Computer-generated scaffolds were either built around an ACE2 helix that interacts with the spike receptor binding domain (RBD) or docked against the RBD to identify new binding modes, and their amino acid sequences were designed to optimize target binding, folding, and stability. Ten designs bound the RBD, with affinities ranging from 100 picomolar to 10 nanomolar, and blocked SARS-CoV-2 infection of Vero E6 cells with median inhibitory concentration (IC50) values between 24 picomolar and 35 nanomolar. The most potent, with new binding modes, are 56- and 64-residue proteins (IC50 ~ 0.16 nanograms per milliliter). Cryo-electron microscopy structures of these minibinders in complex with the SARS-CoV-2 spike ectodomain trimer with all three RBDs bound are nearly identical to the computational models. These hyperstable minibinders provide starting points for SARS-CoV-2 therapeutics.

De novo design of picomolar SARS-CoV-2 miniprotein inhibitors.

3D Variability Analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM.

Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid nanodiscs that are locally disordered. Such spatial variability negatively impacts computational three-dimensional (3D) reconstruction with existing iterative refinement algorithms that assume rigidity. We introduce non-uniform refinement, an algorithm based on cross-validation optimization, which automatically regularizes 3D density maps during refinement to account for spatial variability. Unlike common shift-invariant regularizers, non-uniform refinement systematically removes noise from disordered regions, while retaining signal useful for aligning particle images, yielding dramatically improved resolution and 3D map quality in many cases. We obtain high-resolution reconstructions for multiple membrane proteins as small as 100 kDa, demonstrating increased effectiveness of cryo-EM for this class of targets critical in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package. Membrane proteins exhibit spatial variation in rigidity and disorder, which poses a challenge for traditional cryo-EM reconstruction algorithms. Non-uniform refinement accounts for this spatial variability, yielding improved 3D reconstruction quality even for small membrane proteins.

/pdf/non-uniform-refinement-adaptive-regularization-improves-118wdfu9ey.pdf

Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction.

Single particle cryo-EM is a powerful method for studying proteins and other biological macromolecules. Many of these molecules comprise regions with varying structural properties including disorder, flexibility, and partial occupancy. These traits make computational 3D reconstruction from 2D images challenging. Detergent micelles and lipid nanodiscs, used to keep membrane proteins in solution, are common examples of locally disordered structures that can negatively affect existing iterative refinement algorithms which assume rigidity (or spatial uniformity). We introduce a cross-validation approach to derive non-uniform refinement, an algorithm that automatically regularizes 3D density maps during iterative refinement to account for spatial variability, yielding dramatically improved resolution and 3D map quality. We find that in common iterative refinement methods, regularization using spatially uniform filtering operations can simultaneously over- and under-regularize local regions of a 3D map. In contrast, non-uniform refinement removes noise in disordered regions while retaining signal useful for aligning particle images. Our results include state-of-the-art resolution 3D reconstructions of multiple membrane proteins with molecular weight as low as 90kDa. These results demonstrate that higher resolutions and improved 3D density map quality can be achieved even for small membrane proteins, an important use case for single particle cryo-EM, both in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package and has already been used successfully in several notable structural studies.

/pdf/non-uniform-refinement-adaptive-regularization-improves-318csiwlls.pdf

Non-uniform refinement: Adaptive regularization improves single particle cryo-EM reconstruction

There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.

Methods and systems for 3d structure estimation using non-uniform refinement

1. University of Toronto, Department of Computer Science, Toronto, Canada. 2. Structura Biotechnology Inc., Toronto, Canada. 3. Molecular Structure and Function Program, The Hospital for Sick Children Research Institute, Toronto, Canada. Department of Biochemistry, The University of Toronto, Toronto, Ontario, Canada. Department of Medical Biophysics, The University of Toronto, Toronto, Ontario, Canada. 4. Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada

/pdf/algorithmic-advances-in-single-particle-cryo-em-data-oku7248hrs.pdf

Algorithmic Advances in Single Particle Cryo-EM Data Processing

Convolution neural networks (CNNs) have been implemented with custom hardware on edge devices since its algorithms were successful in many artificial intelligence applications. Although lots of unstructured pruning and mix-bit quantization algorithms have been proposed to successfully compress CNNs, there are few hardware accelerators which can support both sparse and mix-bit CNNs. Besides, sparse matrix computation consumes lots of hardware resources such as registers or BRAM to fetch the needed input activations into processing element (PE). This paper presents a tiny accelerator for mixed-bit sparse CNNs featuring a novel scheme of single vector-based compressed sparse filter (CSF) method and single input multiple output scratch pad (SIMO SPad) to effectively compress weight and fetch the needed input activation. SIMO SPad is shared by multiple PEs, which saves 13.34% CLB LUTs, 46.24% CLB Registers. Furthermore, the accelerator supports mixed-bit sparse computation to obtain better accuracy and performance. When tested on VGG16, compared with 8-bit non-sparsity baselines, the performance of mixed-bit sparsity on Cifar10 and ImageNet improved by 4.85× and 3.33×, respectively, with small accuracy decrease degradation. Compared to state-of-the-art accelerators, the accelerator achieves 1.40× to 2.98× greater DSP efficiency, and offers 1.91× greater energy efficiency. 

Haowei Zhang

Papers

Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction.

Non-uniform refinement: Adaptive regularization improves single particle cryo-EM reconstruction

Methods and systems for 3d structure estimation using non-uniform refinement

Algorithmic Advances in Single Particle Cryo-EM Data Processing

A Tiny Accelerator for Mixed-bit Sparse CNN based on Efficient Fetch Method of SIMO SPad