Selective covalent targeting of SARS-CoV-2 main protease by enantiopure chlorofluoroacetamide

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The main protease (M^pro) is a promising target for COVID-19 treatment. Here, we report an irreversible SARS-CoV-2 M^pro inhibitor possessing chlorofluoroacetamide (CFA) as a warhead for the covalent modification of M^pro. Ugi multicomponent reaction using chlorofluoroacetic acid enabled the rapid synthesis of dipeptidic CFA derivatives that identified 18 as a potent inhibitor of SARS-CoV-2 M^pro. Among the four stereoisomers, (R,R)-18 exhibited a markedly higher inhibitory activity against M^pro than the other isomers. Reaction kinetics and computational docking studies suggest that the R configuration of the CFA warhead is crucial for the rapid covalent inhibition of M^pro. Our findings highlight the prominent influence of the CFA chirality on the covalent modification of proteinous cysteines and provide the basis for improving the potency and selectivity of CFA-based covalent inhibitors.

Chlorofluoroacetamide (CFA) was used as the warhead for covalent targeting of SARS-CoV-2 M^pro. The chirality at CFA showed marked influence on inhibitory activity, suggesting stereospecific activation of CFA for cysteine modification in the protein.     

Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties

SARS-CoV-2, the cause of the COVID-19 pandemic, exploits host cell proteins for viral entry into human lung cells. One of them, the protease TMPRSS2, is required to activate the viral spike protein (S). Even though two inhibitors, camostat and nafamostat, are known to inhibit TMPRSS2 and block cell entry of SARS-CoV-2, finding further potent therapeutic options is still an important task. In this study, we report that a late-stage drug candidate, otamixaban, inhibits SARS-CoV-2 cell entry. We show that otamixaban suppresses TMPRSS2 activity and SARS-CoV-2 infection of a human lung cell line, although with lower potency than camostat or nafamostat. In contrast, otamixaban inhibits SARS-CoV-2 infection of precision cut lung slices with the same potency as camostat. Furthermore, we report that otamixaban''s potency can be significantly enhanced by (sub-) nanomolar nafamostat or camostat supplementation. Dominant molecular TMPRSS2-otamixaban interactions are assessed by extensive 109 μs of atomistic molecular dynamics simulations. Our findings suggest that combinations of otamixaban with supplemental camostat or nafamostat are a promising option for the treatment of COVID-19.

SARS-CoV-2, the cause of the COVID-19 pandemic, exploits host proteins for viral entry into human lung cells and is blocked by otamixaban in combination with a covalent protease inhibitor.     

Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

The main protease (M^pro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural M^pro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural M^pro 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 plasticity at the S2 site. Building on our initial M^pro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective ‘peptibitors’ inhibit M^pro competitively. Our combined results provide new insights and highlight opportunities for the development of M^pro inhibitors as anti-COVID-19 drugs.

The main protease (M^pro) of SARS-CoV-2 is central to viral maturation and is a promising drug target. In silico methods reveal structural aspects of how it binds to its 11 natural cleavage sites, the design of novel peptide inhibitors, and insights into drug design.     

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds.     

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease

The main protease (M^pro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M^pro, a cysteine protease, have been determined, facilitating structure-based drug design. M^pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41–Cys145, M^pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV M^pro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M^pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M^pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N_δ (HD) and N_ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M^pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

The main protease (M^pro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics.     

Iminologous epoxide ring-closure

The discovery of new reactions enables chemists to attain a better understanding of fundamental chemical reactivity and push the boundaries of organic synthesis. Our understanding and manipulation of high-energy states such as reactive conformations, intermediates, and transition structures contribute to this field. Herein we interrogate epoxide ring-closure by inserting the C N functionality into a well-known precursor to nucleophilic epoxide ring-closure. The synthesis of tetrasubstituted, nitrile-tethered epoxides takes place via activation of iminologous diols followed by fragmentation. Mechanistic study reveals the transformation to be stereospecific, which is consistent with the concerted nature of the epoxide ring-closure.

The discovery of new reactions enables chemists to attain a better understanding of fundamental chemical reactivity and push the boundaries of organic synthesis.     

Structure-based de novo drug design using 3D deep generative models

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users'' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.     

Translating daily COVID-19 screening into a simple glucose test: a proof of concept study

Home testing is an attractive emerging strategy to combat the COVID-19 pandemic and prevent overloading of healthcare resources through at-home isolation, screening and monitoring of symptoms. However, current diagnostic technologies of SARS-CoV-2 still suffer from some drawbacks because of the tradeoffs between sensitivity, usability and costs, making the test unaffordable to most users at home. To address these limitations, taking advantage of clustered regularly interspaced short palindromic repeats (CRISPRs) and a portable glucose meter (PGM), we present a proof-of-concept demonstration of a target-responsive CRISPR-PGM system for translating SARS-CoV-2 detection into a glucose test. Using this system, a specific N gene, N protein, and pseudo-viruses of SARS-CoV-2 have been detected quantitatively with a PGM. Given the facile integration of various bioreceptors into the CRISPR-PGM system, the proposed method provides a starting point to provide patients with a single-device solution that can quantitatively monitor multiple COVID-19 biomarkers at home.

COVID-19 glucose test: translating SARS-CoV-2 detection into a glucose test is achieved by incorporating target-responsive rolling circle amplification and a CRISPR-based collateral cleavage module with a portable glucose meter.     

Synchrotron radiation based operando characterization of battery materials

Synchrotron radiation based techniques are powerful tools for battery research and allow probing a wide range of length scales, with different depth sensitivities and spatial/temporal resolutions. Operando experiments enable characterization during functioning of the cell and are thus a precious tool to elucidate the reaction mechanisms taking place. In this perspective, the current state of the art for the most relevant techniques (scattering, spectroscopy, and imaging) is discussed together with the bottlenecks to address, either specific for application in the battery field or more generic. The former includes the improvement of cell designs, multi-modal characterization and development of protocols for automated or at least semi-automated data analysis to quickly process the huge amount of data resulting from operando experiments. Given the recent evolution in these areas, accelerated progress is expected in the years to come, which should in turn foster battery performance improvements.

Synchrotron radiation enables probing a wide range of length scales operando, hence being a powerful tool in battery research. Challenges ahead involve cell design (especially for multi-modal approaches) and protocols for automated data analysis.     

Covalent flexible peptide docking in Rosetta

Electrophilic peptides that form an irreversible covalent bond with their target have great potential for binding targets that have been previously considered undruggable. However, the discovery of such peptides remains a challenge. Here, we present Rosetta CovPepDock, a computational pipeline for peptide docking that incorporates covalent binding between the peptide and a receptor cysteine. We applied CovPepDock retrospectively to a dataset of 115 disulfide-bound peptides and a dataset of 54 electrophilic peptides. It produced a top-five scoring, near-native model, in 89% and 100% of the cases when docking from the native conformation, and 20% and 90% when docking from an extended peptide conformation, respectively. In addition, we developed a protocol for designing electrophilic peptide binders based on known non-covalent binders or protein–protein interfaces. We identified 7154 peptide candidates in the PDB for application of this protocol. As a proof-of-concept we validated the protocol on the non-covalent complex of 14-3-3σ and YAP1 phosphopeptide. The protocol identified seven highly potent and selective irreversible peptide binders. The predicted binding mode of one of the peptides was validated using X-ray crystallography. This case-study demonstrates the utility and impact of CovPepDock. It suggests that many new electrophilic peptide binders can be rapidly discovered, with significant potential as therapeutic molecules and chemical probes.

We developed Rosetta CovPepDock, a computational pipeline for covalent peptide docking. We showed it is highly accurate in retrospective benchmarks, and applied it prospectively to design potent and selective covalent binders of 14-3-3σ.     

Discovery of a photochemical cascade process by flow-based interception of isomerising alkenes

Herein we report the discovery of a new photochemical cascade process through a flow-based strategy for intercepting diradicals generated from simple alkenes. This continuous process delivers a series of unprecedented polycyclic reaction products. Exploring the scope of this novel process revealed that this approach is general and affords a variety of structurally complex reaction products in high yields (up to 81%), short reaction times (7 min) and high throughputs (up to 5.5 mmol h⁻¹). A mechanistic rationale is presented that is supported by computations as well as isolation of key intermediates whose identity is confirmed by X-ray crystallography. The presented photochemical cascade process demonstrates the discovery of new chemical reactivity and complex chemical scaffolds by continuously generating and intercepting high-energy intermediates in a highly practical manner.

A photochemical cascade process is reported affording complex pentacyclic scaffolds in high yields from readily available substrates. Flow processing provided high reaction control and scalability to generate gram quantities of these intriguing scaffolds for further studies.     

A framework for automated structure elucidation from routine NMR spectra

Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery. NMR spectroscopy is the most widely used and arguably the most powerful method for elucidating structures of organic molecules. Here we introduce a machine learning (ML) framework that provides a quantitative probabilistic ranking of the most likely structural connectivity of an unknown compound when given routine, experimental one dimensional ¹H and/or ¹³C NMR spectra. In particular, our ML-based algorithm takes input NMR spectra and (i) predicts the presence of specific substructures out of hundreds of substructures it has learned to identify; (ii) annotates the spectrum to label peaks with predicted substructures; and (iii) uses the substructures to construct candidate constitutional isomers and assign to them a probabilistic ranking. Using experimental spectra and molecular formulae for molecules containing up to 10 non-hydrogen atoms, the correct constitutional isomer was the highest-ranking prediction made by our model in 67.4% of the cases and one of the top-ten predictions in 95.8% of the cases. This advance will aid in solving the structure of unknown compounds, and thus further the development of automated structure elucidation tools that could enable the creation of fully autonomous reaction discovery platforms.

A machine learning model and graph generator were able to accurately predict for the presence of nearly 1000 substructures and the connectivity of small organic molecules from experimental 1D NMR data.     

Intramolecular photochemical [2 + 1]-cycloadditions of nucleophilic siloxy carbenes

Visible light induced singlet nucleophilic carbenes undergo rapid [2 + 1]-cycloaddition with tethered olefins to afford unique bicyclo[3.1.0]hexane and bicyclo[4.1.0]heptane scaffolds. This cyclopropanation process requires only visible light irradiation to proceed, circumventing the use of exogenous (photo)catalysts, sensitisers or additives and showcases a vastly underexplored mode of reactivity for nucleophilic carbenes in chemical synthesis. The discovery of additional transformations including a cyclopropanation/retro-Michael/Michael cascade process to afford chromanones and a photochemical C–H insertion reaction are also described.

Visible light induced singlet nucleophilic carbenes undergo rapid [2 + 1]-cycloaddition with tethered olefins to afford unique bicyclo[3.1.0]hexane and bicyclo[4.1.0]heptane scaffolds.     

In-solution direct oxidative coupling for the integration of sulfur/selenium into DNA-encoded chemical libraries

Sulfur/selenium-containing electron-rich arenes (ERAs) exist in a wide range of both approved and investigational drugs with diverse pharmacological activities. These unique chemical structures and bioactive properties, if combined with the emerging DNA-encoded chemical library (DEL) technique, would facilitate drug and chemical probe discovery. However, it remains challenging, as there is no general DNA-compatible synthetic methodology available for the formation of C–S and C–Se bonds in aqueous solution. Herein, an in-solution direct oxidative coupling procedure that could efficiently integrate sulfur/selenium into the ERA under mild conditions is presented. This method features simple DNA-conjugated electron-rich arenes with a broad substrate scope and a transition-metal free process. Furthermore, this synthetic methodology, examined by a scale-up reaction test and late-stage precise modification in a mock peptide-like DEL synthesis, will enable its utility for the synthesis of sulfur/selenium-containing DNA-encoded libraries and the discovery of bioactive agents.

DNA-compatible direct oxidative coupling using various sulfur/selenium sources has been achieved, featuring pre-functionalization-free substrates and transition metal-free condition.     

Automation isn't automatic

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chemical systems. Careful assessment of these critical procedural requirements and identification of the tools suitable for effective experimental execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.

The process of automating chemistry involves a wide variety of considerations that are often overlooked.     

Exhaustive Baeyer–Villiger oxidation: a tailor-made post-polymerization modification to access challenging poly(vinyl acetate) copolymers

The discovery of exhaustive (nearly quantitative) post-polymerization modifications (PPM) relies heavily on the efficiency of their corresponding small-molecule protocols. However, the direct translation of existing small-molecule protocols into PPM methods has never been guaranteed due to the intrinsic differences between small-molecule substrates and polymers. Herein, we introduce the direct optimization on polymers (DOP) as a complementary approach to developing exhaustive PPM reactions. As proof of the DOP concept, we present an exhaustive Baeyer–Villiger (BV) post-modification which cannot be accessed by conventional approaches. This user-friendly methodology provides general access to synthetically challenging copolymers of vinyl acetate and more activated monomers (MAMs) including both statistical and narrow-dispersed block copolymers. Furthermore, a scalable one-pot copolymerization/exhaustive BV post-modification procedure was developed to produce such materials showing improved performance over regular PVAc.

Exhaustive Baeyer–Villiger (BV) oxidation, which was developed by a direct optimization on polymers (DOP) approach, provides a general solution for preparing synthetically challenging poly(vinyl acetate) statistical and block copolymers.     

Automated affinity selection for rapid discovery of peptide binders

In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be time-consuming, low-throughput, or provide hits that display low selectivity to the target. Here we report an automated bio-layer interferometry (BLI)-assisted affinity selection platform. When coupled with tandem mass spectrometry (MS), this method enables both rapid de novo discovery and affinity maturation of known peptide binders with high selectivity. The BLI-assisted AS-MS technology also features real-time monitoring of the peptide binding during the library selection process, a feature unattainable by current selection approaches. We show the utility of the BLI AS-MS platform toward rapid identification of novel nanomolar (dissociation constant, K_D < 50 nM) non-canonical binders to the leukemia-associated oncogenic protein menin. To our knowledge, this is the first application of BLI to the affinity selection of synthetic peptide libraries. We believe our approach can significantly accelerate the use of synthetic peptidomimetic libraries in drug discovery.

This work reports an automated affinity selection-mass spectrometry (AS-MS) approach amenable to both de novo peptide binder discovery and affinity maturation of known binders in a high-throughput and selective manner.     

Determination of intermediate state structures in the opening pathway of SARS-CoV-2 spike using cryo-electron microscopy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19, a highly infectious disease that is severely affecting our society and welfare systems. In order to develop therapeutic interventions against this condition, one promising strategy is to target spike, the trimeric transmembrane glycoprotein that the virus uses to recognise and bind its host cells. Here we use a metainference cryo-electron microscopy approach to determine the opening pathway that brings spike from its inactive (closed) conformation to its active (open) one. The knowledge of the structures of the intermediate states of spike along this opening pathway enables us to identify a cryptic pocket that is not exposed in the open and closed states. These results underline the opportunities offered by the determination of the structures of the transient intermediate states populated during the dynamics of proteins to allow the therapeutic targeting of otherwise invisible cryptic binding sites.

A structural ensemble derived from cryo-electron microscopy reveals a cryptic pocket site in intermediate states along the opening pathway of the SARS-CoV-2 spike protein.     

Orally administered bismuth drug together with N-acetyl cysteine as a broad-spectrum anti-coronavirus cocktail therapy

The emergence of SARS-CoV-2 variants of concern compromises vaccine efficacy and emphasizes the need for further development of anti-SARS-CoV-2 therapeutics, in particular orally administered take-home therapies. Cocktail therapy has shown great promise in the treatment of viral infection. Herein, we reported the potent preclinical anti-SARS-CoV-2 efficacy of a cocktail therapy consisting of clinically used drugs, e.g. colloidal bismuth subcitrate (CBS) or bismuth subsalicylate (BSS), and N-acetyl-l-cysteine (NAC). Oral administration of the cocktail reduced viral loads in the lung and ameliorated virus-induced pneumonia in a hamster infection model. The mechanistic studies showed that NAC prevented the hydrolysis of bismuth drugs at gastric pH via the formation of the stable component [Bi(NAC)₃], and optimized the pharmacokinetics profile of CBS in vivo. Combination of bismuth drugs with NAC suppressed the replication of a panel of medically important coronaviruses including Middle East respiratory syndrome-related coronavirus (MERS-CoV), Human coronavirus 229E (HCoV-229E) and SARS-CoV-2 Alpha variant (B.1.1.7) with broad-spectrum inhibitory activities towards key viral cysteine enzymes/proteases including papain-like protease (PL^pro), main protease (M^pro), helicase (Hel) and angiotensin-converting enzyme 2 (ACE2). Importantly, our study offered a potential at-home treatment for combating SARS-CoV-2 and future coronavirus infections.

A cocktail therapy comprising bismuth drugs and N-acetyl-l-cysteine is reported to suppress the replication of SARS-CoV-2 via the oral route. The broad-spectrum inhibitory activities of the combination upon key viral cysteine enzymes are verified.