Studies on the selectivity of the SARS-CoV-2 papain-like protease reveal the importance of the P2' proline of the viral polyprotein (preprint)

The SARS-CoV-2 papain-like protease (PLpro) is an antiviral drug target that catalyzes the hydrolysis of the viral polyproteins pp1a/1ab, releasing the non-structural proteins (nsps) 1-3 that are essential for the coronavirus lifecycle. The LXGG{downarrow}X motif found in pp1a/1ab is crucial for recognition and cleavage by PLpro. We describe molecular dynamics, docking, and quantum mechanics/molecular mechanics (QM/MM) calculations to investigate how oligopeptide substrates derived from the viral polyprotein bind to PLpro. The results reveal how the substrate sequence affects the efficiency of PLpro-catalyzed hydrolysis. In particular, a proline at the P2'; position promotes catalysis, as validated by residue substitutions and mass spectrometry-based analyses. Analysis of PLpro catalyzed hydrolysis of LXGG motif-containing oligopeptides derived from human proteins suggests that factors beyond the LXGG motif and the presence of a proline residue at P2' contribute to catalytic efficiency, possibly reflecting the promiscuity of PLpro. The results will help in identifying PLpro substrates and guiding inhibitor design.

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework (preprint)

The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets — the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework (preprint)

The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets -- the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

xia2.multiplex: a multi-crystal data analysis pipeline (preprint)

In macromolecular crystallography radiation damage limits the amount of data that can be collected from a single crystal. It is often necessary to merge data sets from multiple crystals, for example small-wedge data collections on micro-crystals, in situ room-temperature data collections, and collection from membrane proteins in lipidic mesophase. Whilst indexing and integration of individual data sets may be relatively straightforward with existing software, merging multiple data sets from small wedges presents new challenges. Identification of a consensus symmetry can be problematic, particularly in the presence of a potential indexing ambiguity. Furthermore, the presence of non-isomorphous or poor-quality data sets may reduce the overall quality of the final merged data set. To facilitate and help optimise the scaling and merging of multiple data sets, we developed a new program, xia2.multiplex , which takes data sets individually integrated with DIALS and performs symmetry analysis, scaling and merging of multicrystal data sets. xia2.multiplex also performs analysis of various pathologies that typically affect multi-crystal data sets, including non-isomorphism, radiation damage and preferential orientation. After describing a number of use cases, we demonstrate the benefit of xia2.multiplex within a wider autoprocessing framework in facilitating a multi-crystal experiment collected as part of in situ room-temperature fragment screening experiments on the SARS-CoV-2 main protease.

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

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 (preprint)

Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

A cell-free antibody engineering platform rapidly generates SARS-CoV-2 neutralizing antibodies (preprint)

Antibody engineering technologies face increasing demands for speed, reliability and scale. We developed CeVICA, a cell-free antibody engineering platform that integrates a novel generation method and design for camelid heavy-chain antibody VHH domain-based synthetic libraries, optimized in vitro selection based on ribosome display and a computational pipeline for binder prediction based on CDR-directed clustering. We applied CeVICA to engineer antibodies against the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike proteins and identified >800 predicted binder families. Among 14 experimentally-tested binders, 6 showed inhibition of pseudotyped virus infection. Antibody affinity maturation further increased binding affinity and potency of inhibition. Additionally, the unique capability of CeVICA for efficient and comprehensive binder prediction allowed retrospective validation of the fitness of our synthetic VHH library design and revealed direction for future refinement. CeVICA offers an integrated solution to rapid generation of divergent synthetic antibodies with tunable affinities in vitro and may serve as the basis for automated and highly parallel antibody generation.

PRAK-03202: A triple antigen VLP vaccine candidate against SARS CoV-2 (preprint)

The rapid development of safe and effective vaccines against SARS CoV-2 is the need of the hour for the coronavirus outbreak. Here, we have developed PRAK-03202, the world's first triple antigen VLP vaccine candidate in a highly characterized S. cerevisiae-based D-Crypt platform, which induced SARS CoV-2 specific neutralizing antibodies in BALB/c mice. Immunizations using three different doses of PRAK-03202 induces antigen specific (Spike, envelope and membrane proteins) humoral response and neutralizing potential. PBMCs from convalescent patients, when exposed to PRAK-03202, showed lymphocyte proliferation and elevated IFN-{gamma} levels suggestive of conservation of epitopes and induction of T helper 1 (Th1)-biased cellular immune responses. These data support the clinical development and testing of PRAK-03202 for use in humans.

Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses (preprint)

Seasonal coronaviruses (OC43, 229E, NL63 and HKU1) are endemic to the human population, regularly infecting and reinfecting humans while typically causing asymptomatic to mild respiratory infections. It is not known to what extent reinfection by these viruses is due to waning immune memory or antigenic drift of the viruses. Here, we address the influence of antigenic drift on immune evasion of seasonal coronaviruses. We provide evidence that at least two of these viruses, OC43 and 229E, are undergoing adaptive evolution in regions of the viral spike protein that are exposed to human humoral immunity. This suggests that reinfection may be due, in part, to positively-selected genetic changes in these viruses that enable them to escape recognition by the immune system. It is possible that, as with seasonal influenza, these adaptive changes in antigenic regions of the virus would necessitate continual reformulation of a vaccine made against them.

An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor. (preprint)

Designing covalent inhibitors is a task of increasing importance in drug discovery. Efficiently designing irreversible inhibitors, though, remains challenging. Here, we present covalentizer, a computational pipeline for creating irreversible inhibitors based on complex structures of targets with known reversible binders. For each ligand, we create a custom-made focused library of covalent analogs. We use covalent docking, to dock these tailored covalent libraries and to find those that can bind covalently to a nearby cysteine while keeping some of the main interactions of the original molecule. We found ~11,000 cysteines in close proximity to a ligand across 8,386 protein-ligand complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In prospective evaluation against a panel of kinases, five out of nine predicted covalent inhibitors showed IC50 between 155 nM - 4.2 M. Application of the protocol to an existing SARS-CoV-1 Mpro reversible inhibitor led to a new acrylamide inhibitor series with low micromolar IC50 against SARS-CoV-2 Mpro. The docking prediction was validated by 11 co-crystal structures. This is a promising lead series for COVID-19 antivirals. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.

Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease (preprint)

COVID-19, caused by SARS-CoV-2, lacks effective therapeutics. Additionally, no antiviral drugs or vaccines were developed against the closely related coronavirus, SARS-CoV-1 or MERS-CoV, despite previous zoonotic outbreaks. To identify starting points for such therapeutics, we performed a large-scale screen of electrophile and non-covalent fragments through a combined mass spectrometry and X-ray approach against the SARS-CoV-2 main protease, one of two cysteine viral proteases essential for viral replication. Our crystallographic screen identified 71 hits that span the entire active site, as well as 3 hits at the dimer interface. These structures reveal routes to rapidly develop more potent inhibitors through merging of covalent and non-covalent fragment hits; one series of low-reactivity, tractable covalent fragments was progressed to discover improved binders. These combined hits offer unprecedented structural and reactivity information for on-going structure-based drug design against SARS-CoV-2 main protease.