Crystal Structures of Inhibitor-Bound Main Protease from Delta- and Gamma-Coronaviruses.

With the spread of SARS-CoV-2 throughout the globe causing the COVID-19 pandemic, the threat of zoonotic transmissions of coronaviruses (CoV) has become even more evident. As human infections have been caused by alpha- and beta-CoVs, structural characterization and inhibitor design mostly focused on these two genera. However, viruses from the delta and gamma genera also infect mammals and pose a potential zoonotic transmission threat. Here, we determined the inhibitor-bound crystal structures of the main protease (Mpro) from the delta-CoV porcine HKU15 and gamma-CoV SW1 from the beluga whale. A comparison with the apo structure of SW1 Mpro, which is also presented here, enabled the identification of structural arrangements upon inhibitor binding at the active site. The cocrystal structures reveal binding modes and interactions of two covalent inhibitors, PF-00835231 (active form of lufotrelvir) bound to HKU15, and GC376 bound to SW1 Mpro. These structures may be leveraged to target diverse coronaviruses and toward the structure-based design of pan-CoV inhibitors.

Quantitative structural analysis of influenza virus by cryo-electron tomography and convolutional neural networks.

Influenza viruses pose severe public health threats globally. Influenza viruses are extensively pleomorphic, in shape, size, and organization of viral proteins. Analysis of influenza morphology and ultrastructure can help elucidate viral structure-function relationships and aid in therapeutics and vaccine development. While cryo-electron tomography (cryoET) can depict the 3D organization of pleomorphic influenza, the low signal-to-noise ratio inherent to cryoET and viral heterogeneity have precluded detailed characterization of influenza viruses. In this report, we leveraged convolutional neural networks and cryoET to characterize the morphological architecture of the A/Puerto Rico/8/34 (H1N1) influenza strain. Our pipeline improved the throughput of cryoET analysis and accurately identified viral components within tomograms. Using this approach, we successfully characterized influenza morphology, glycoprotein density, and conducted subtomogram averaging of influenza glycoproteins. Application of this processing pipeline can aid in the structural characterization of not only influenza viruses, but other pleomorphic viruses and infected cells.