AI-Driven Discovery of SARS-CoV-2 Main Protease Fragment-like Inhibitors with Antiviral Activity In Vitro.

SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp's, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 µM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.

Structural characterization of the Streptococcus pneumoniae carbohydrate substrate-binding protein SP0092.

Streptococcus pneumoniae is an opportunistic respiratory pathogen that remains a major cause of morbidity and mortality globally, with infants and the elderly at the highest risk. S. pneumoniae relies entirely on carbohydrates as a source of carbon and dedicates a third of all uptake systems to carbohydrate import. The structure of the carbohydrate-free substrate-binding protein SP0092 at 1.61 Šresolution reveals it to belong to the newly proposed subclass G of substrate-binding proteins, with a ligand-binding pocket that is large enough to accommodate complex oligosaccharides. SP0092 is a dimer in solution and the crystal structure reveals a domain-swapped dimer with the monomer subunits in a closed conformation but in the absence of carbohydrate ligand. This closed conformation may be induced by dimer formation and could be used as a mechanism to regulate carbohydrate uptake.

SynchLink: an iOS app for ISPyB.

The macromolecular crystallography (MX) user experience at synchrotron radiation facilities continues to evolve, with the impact of developments in X-ray detectors, computer hardware and automation methods making it possible for complete data sets to be collected on timescales of tens of seconds. Data can be reduced in a couple of minutes and in favourable cases structures solved and refined shortly after. The information-rich database ISPyB, automatically populated by data acquisition software, data processing and structure solution pipelines at the Diamond Light Source beamlines, allows users to automatically track MX experiments in real time. In order to improve the synchrotron users' experience, efficient access to the data contained in ISPyB is now provided via an iOS 6.0+ app for iPhones and iPads. This provides users, both local and remote, with a succinct summary of data collection, visualization of diffraction images and crystals, and key metrics for data quality in real time.