Allosteric inhibition of the SARS-CoV-2 main protease - insights from mass spectrometry-based assays

Following translation of the SARS-CoV-2 RNA genome into two viral polypeptides, the main protease Mpro cleaves at eleven sites to release non-structural proteins required for viral replication. MPro is an attractive target for antiviral therapies to combat the coronavirus-2019 disease (COVID-19). Here, we have used native mass spectrometry (MS) to characterize the functional unit of Mpro. Analysis of the monomer-dimer equilibria reveals a dissociation constant of Kd = 0.14 {+/-} 0.03 M, revealing MPro has a strong preference to dimerize in solution. Developing an MS-based kinetic assay we then characterized substrate turnover rates by following temporal changes in the enzyme-substrate complexes, which are effectively "flash-frozen" as they transition from solution to the gas phase. We screened small molecules, that bind distant from the active site, for their ability to modulate activity. These compounds, including one proposed to disrupt the catalytically active dimer, slow the rate of substrate processing by ~35%. This information was readily obtained and, together with analysis of the x-ray crystal structures of these enzyme-small molecule complexes, provides a starting point for the development of more potent molecules that allosterically regulate MPro activity.

Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding

The main protease (Mpro) of SARS-CoV-2 is central to its viral lifecycle and is a promising drug target, but little is known concerning structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of classical molecular mechanics and quantum mechanical techniques, including automated docking, molecular dynamics (MD) simulations, linear-scaling DFT, QM/MM, and interactive MD in virtual reality, to investigate the molecular features underlying recognition of the natural Mpro substrates. Analyses of the subsite interactions of modelled 11-residue cleavage site peptides, ligands from high-throughput crystallography, and designed covalently binding inhibitors were performed. Modelling studies reveal remarkable conservation of hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular at the P2/S2 sites. The binding modes of the natural substrates, together with extensive interaction analyses of inhibitor and fragment binding to Mpro, reveal new opportunities for inhibition. Building on our initial Mpro-substrate models, computational mutagenesis scanning was employed to design peptides with improved affinity and which inhibit Mpro competitively. The combined results provide new insight useful for the development of Mpro inhibitors.

COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated from current clinical candidates in terms of toxicity, resistance, and pharmacokinetics liabilities, and developed it into noncovalent orally-bioavailable nanomolar inhibitors with clinical potential. Our approach leveraged crowdsourcing, high-throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.