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Highlights of Coronavirus Structural Studies

6 Apr 2022

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (PNAS)

The ability of viruses to mutate and evade the human immune system and neutralizing antibodies remains an obstacle to antiviral and vaccine development. Many neutralizing antibodies, including some approved for emergency use authorization (EUA), reduced or lost activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Here, we introduce a geometric deep learning algorithm that efficiently enhances antibody affinity to achieve broader and more potent neutralizing activity against such variants. We demonstrate the utility of our approach on a human antibody P36-5D2, which is effective against SARS-CoV-2 Alpha, Beta, and Gamma but not Delta. We show that our geometric neural network model optimizes this antibody's complementaritydetermining region (CDR) sequences to improve its binding affinity against multiple SARS-CoV-2 variants. Through iterative optimization of the CDR regions and experimental measurements, we enable expanded antibody breadth and improved potency by similar to 10to 600-fold against SARS-CoV-2 variants, including Delta. We have also demonstrated that our approach can identify CDR changes that alleviate the impact of two Omicron mutations on the epitope. These results highlight the power of our deep learning approach in antibody optimization and its potential application to engineering other protein molecules. Our optimized antibodies can potentially be developed into antibody drug candidates for current and emerging SARS-CoV-2 variants.

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Reader's Corner Archive

9 Dec 2022

Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN (Nature Protocols)

Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single-particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find is effective in modeling both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for the analysis of an existing assembling 50S ribosome dataset, including preparation of inputs, network training and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single-particle cryo-EM datasets and with moderate experience navigating Python and Jupyter notebooks. It requires 3–4 days to complete. CryoDRGN is open source software that is freely available.

6 Dec 2022

Recognition of cyclic dinucleotides folates by human SLC19A1 (Nature)

Cyclic dinucleotides (CDNs) are ubiquitous signalling molecules in all domains of life. Mammalian cells produce one CDN, 2′3′-cGAMP, through cyclic GMP–AMP synthase after detecting cytosolic DNA signals. 2′3′-cGAMP, as well as bacterial and synthetic CDN analogues, can act as second messengers to activate stimulator of interferon genes (STING) and elicit broad downstream responses. Extracellular CDNs must traverse the cell membrane to activate STING, a process that is dependent on the solute carrier SLC19A. Moreover, SLC19A1 represents the major transporter for folate nutrients and antifolate therapeutics, thereby placing SLC19A1 as a key factor in multiple physiological and pathological processes. How SLC19A1 recognizes and transports CDNs, folate and antifolate is unclear. Here we report cryo-electron microscopy structures of human SLC19A1 (hSLC19A1) in a substrate-free state and in complexes with multiple CDNs from different sources, a predominant natural folate and a new-generation antifolate drug. The structural and mutagenesis results demonstrate that hSLC19A1 uses unique yet divergent mechanisms to recognize CDN- and folate-type substrates. Two CDN molecules bind within the hSLC19A1 cavity as a compact dual-molecule unit, whereas folate and antifolate bind as a monomer and occupy a distinct pocket of the cavity. Moreover, the structures enable accurate mapping and potential mechanistic interpretation of hSLC19A1 with loss-of-activity and disease-related mutations. Our research provides a framework for understanding the mechanism of SLC19-family transporters and is a foundation for the development of potential therapeutics.

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