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

Exploring of N-phthalimide-linked 1,2,3-triazole analogues with promising -anti-SARS-CoV-2 activity: synthesis, biological screening, and molecular modelling studies

In this study, a library of phthalimide Schiff base linked to 1,4-disubstituted-1,2,3-triazoles was designed, synthesised, and characterised by different spectral analyses. All analogues have been introduced for in vitro assay of their antiviral activity against COVID-19 virus using Vero cell as incubator with different concentrations. The data revealed most of these derivatives showed potent cellular anti-COVID-19 activity and prevent viral growth by more than 90% at two different concentrations with no or weak cytotoxic effect on Vero cells. Furthermore, in vitro assay was done against this enzyme for all analogues and the results showed two of them have IC50 data by 90 µM inhibitory activity. An extensive molecular docking simulation was run to analyse their antiviral mechanism that found the proper non-covalent interaction within the Mpro protease enzyme. Finally, we profiled two reversible inhibitors, COOH and F substituted analogues that might be promising drug candidates for further development have been discovered.

Structural basis of rosmarinic acid inhibitory mechanism on SARS-CoV-2 main protease

The SARS-CoV-2 coronavirus is characterized by high mutation rates and significant infectivity, posing ongoing challenges for therapeutic intervention. To address potential challenges in the future, the continued development of effective drugs targeting SARS-CoV-2 remains an important task for the scientific as well as the pharmaceutical community. The main protease (Mpro) of SARS-CoV-2 is an ideal therapeutic target for COVID-19 drug development, leading to the introduction of various inhibitors, both covalent and non-covalent, each characterized by unique mechanisms of action and possessing inherent strengths and limitations. Natural products, being compounds naturally present in the environment, offer advantages such as low toxicity and diverse activities, presenting a viable source for antiviral drug development. Here, we identified a natural compound, rosmarinic acid, which exhibits significant inhibitory effects on the Mpro of the SARS-CoV-2. Through detailed structural biology analysis, we elucidated the precise crystal structure of the complex formed between rosmarinic acid and SARS-CoV-2 Mpro, revealing the molecular basis of its inhibitory mechanism. These findings not only enhance our understanding of the antiviral action of rosmarinic acid, but also provide valuable structural information and mechanistic insights for the further development of therapeutic strategies against SARS-CoV-2.

3-chymotrypsin-like protease in SARS-CoV-2

Coronaviruses constitute a significant threat to the human population. Severe acute respiratory syndrome coronavirus-2, SARS-CoV-2, is a highly pathogenic human coronavirus that has caused the coronavirus disease 2019 (COVID-19) pandemic. It has led to a global viral outbreak with an exceptional spread and a high death toll, highlighting the need for effective antiviral strategies. 3-Chymotrypsin-like protease (3CLpro), the main protease in SARS-CoV-2, plays an indispensable role in the SARS-CoV-2 viral life cycle by cleaving the viral polyprotein to produce 11 individual non-structural proteins necessary for viral replication. 3CLpro is one of two proteases that function to produce new viral particles. It is a highly conserved cysteine protease with identical structural folds in all known human coronaviruses. Inhibitors binding with high affinity to 3CLpro will prevent the cleavage of viral polyproteins, thus impeding viral replication. Multiple strategies have been implemented to screen for inhibitors against 3CLpro, including peptide-like and small molecule inhibitors that covalently and non-covalently bind the active site, respectively. In addition, allosteric sites of 3CLpro have been identified to screen for small molecules that could make non-competitive inhibitors of 3CLpro. In essence, this review serves as a comprehensive guide to understanding the structural intricacies and functional dynamics of 3CLpro, emphasizing key findings that elucidate its role as the main protease of SARS-CoV-2. Notably, the review is a critical resource in recognizing the advancements in identifying and developing 3CLpro inhibitors as effective antiviral strategies against COVID-19, some of which are already approved for clinical use in COVID-19 patients.

An expanded trove of fragment-bound structures for the allosteric enzyme PTP1B from computational reanalysis of large-scale crystallographic data

Due to their low binding affinities, detecting small-molecule fragments bound to protein structures from crystallographic datasets has been a challenge. Here, we report a trove of 65 new fragment hits for PTP1B, an "undruggable" therapeutic target enzyme for diabetes and cancer. These structures were obtained from computational analysis of data from a large crystallographic screen, demonstrating the power of this approach to elucidate many (∼50% more) "hidden" ligand-bound states of proteins. Our new structures include a fragment hit found in a novel binding site in PTP1B with a unique location relative to the active site, one that links adjacent allosteric sites, and, perhaps most strikingly, a fragment that induces long-range allosteric protein conformational responses. Altogether, our research highlights the utility of computational analysis of crystallographic data, makes publicly available dozens of new ligand-bound structures of a high-value drug target, and identifies novel aspects of ligandability and allostery in PTP1B.

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Fragment-based drug discovery campaigns guided by native mass spectrometry

Native mass spectrometry (nMS) is well established as a biophysical technique for characterising biomolecules and their interactions with endogenous or investigational small molecule ligands. The high sensitivity mass measurements make nMS particularly well suited for applications in fragment-based drug discovery (FBDD) screening campaigns where the detection of weakly binding ligands to a target biomolecule is crucial. We first reviewed the contributions of nMS to guiding FBDD hit identification in 2013, providing a comprehensive perspective on the early adoption of nMS for fragment screening. Here we update this initial progress with a focus on contributions of nMS that have guided FBDD for the period 2014 until end of 2023. We highlight the development of nMS adoption in FBDD in the context of other biophysical fragment screening techniques. We also discuss the roadmap for increased adoption of nMS for fragment screening beyond soluble proteins, including for guiding the discovery of fragments supporting advances in PROTAC discovery, RNA-binding small molecules and covalent therapeutic drug discovery.

In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus

In 2019, the emergence of the seventh known coronavirus to cause severe illness in humans triggered a global effort towards the development of new drugs and vaccines for the SARS-CoV-2 virus. These efforts are still ongoing in 2024, including the present work where we conducted a ligand-based virtual screening of terpenes with potential anti-SARS-CoV-2 activity. We constructed a Quantitative Structure-Activity Relationship (QSAR) model from compounds with known activity against SARS-CoV-2 with a model accuracy of 0.71. We utilized this model to predict the activity of a series of 217 terpenes isolated from the Fabaceae family. Four compounds, predominantly triterpenoids from the lupane series, were subjected to an in vitro phenotypic screening in Vero CCL-81 cells to assess their inhibitory activity against SARS-CoV-2. The compounds which showed high rates of SARS-CoV-2 inhibition along with substantial cell viability underwent molecular docking at the SARS-CoV-2 main protease, papain-like protease, spike protein and RNA-dependent RNA polymerase. Overall, virtual screening through our QSAR model successfully identified compounds with the highest probability of activity, as validated using the in vitro study. This confirms the potential of the identified triterpenoids as promising candidates for anti-SARS-CoV-2 therapeutics.

Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

Residues in the fructose-binding pocket are required for ketohexokinase-A activity

Excessive fructose consumption is a primary contributor to the global surges in obesity, cancer, and metabolic syndrome. Fructolysis is not robustly regulated and is initiated by ketohexokinase (KHK). In this study, we determined the crystal structure of KHK-A, one of two human isozymes of KHK, in the apo-state at 1.85 Å resolution, and we investigated the roles of residues in the fructose-binding pocket by mutational analysis. Introducing alanine at D15, N42, or N45 inactivated KHK-A, whereas mutating R141 or K174 reduced activity and thermodynamic stability. Kinetic studies revealed that the R141A and K174A mutations reduced fructose affinity by 2- to 4-fold compared to WT KHK-A, without affecting ATP affinity. Molecular dynamics simulations provided mechanistic insights into the potential roles of the mutated residues in ligand coordination and the maintenance of an open state in one monomer and a closed state in the other. Protein-protein interactome analysis indicated distinct expression patterns and downregulation of partner proteins in different tumor tissues, warranting a reevaluation of KHK's role in cancer development and progression. The connections between different cancer genes and the KHK signaling pathway suggest that KHK is a potential target for preventing cancer metastasis. This study enhances our understanding of KHK-A's structure and function and offers valuable insights into potential targets for developing treatments for obesity, cancer, and metabolic syndrome.

N-Phenylnorbornenesuccinimide derivatives, agricultural defensive, and enzymatic target selection

BACKGROUND

Faced with the need to develop new herbicides with modes of action different to those observed for existing agrochemicals, one of the most promising strategies employed by synthetic chemists involves the structural modification of molecules found in natural products. Molecules containing amides, imides, and epoxides as functional groups are prevalent in nature and find extensive application in synthesizing more intricate compounds due to their biological properties. In this context, this paper delineates the synthesis of N-phenylnorbornenesuccinimide derivatives, conducts biological assays, and carries out in silico investigation of the protein target associated with the most potent compound in plant organisms. The phytotoxic effects of the synthesized compounds (2-29) were evaluated on Allium cepa, Bidens pilosa, Cucumis sativus, Sorghum bicolor, and Solanum lycopersicum.

RESULTS

Reaction of endo-bicyclo[2.2.1]hept-5-ene-3a,7a-dicarboxylic anhydride (1) with aromatic amines led to the N-phenylnorbornenesuccinic acids (2-11) with yields ranging from 75% to 90%. Cyclization of compounds (2-11) in the presence of acetic anhydride and sodium acetate afforded N-phenylnorbornenesuccinimides (12-20) with yields varying from 65% to 89%. Those imides were then subjected to epoxidation reaction to afford N-phenylepoxynorbornanesuccimides (21-29) with yields from 60% to 90%. All compounds inhibited the growth of seedlings of the plants evaluated. Substance 23 was the most active against the plants tested, inhibiting 100% the growth of all species in all concentrations. Cyclophilin was found to be the enzymatic target of compound 23.

CONCLUSION

These findings suggest that derivatives of N-phenylnorbornenesuccinimide are promising compounds in the quest for more selective and stable agrochemicals. This perspective reinforces the significance of these derivatives as potential innovative herbicides and emphasizes the importance of further exploring their biological activity on weeds. © 2024 Society of Chemical Industry.

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

In silico screening of potential inhibitors from Cordyceps species against SARS-CoV-2 main protease

Coronavirus disease 2019 (COVID-19) is a result of a retroviral infection of SARS-CoV-2. Due to its virulence and high infection rate, it is a matter of serious concern and a global health emergency. Currently available COVID-19 vaccines approved by regulatory bodies around the world have been shown to provide significant protection against COVID-19. But no vaccine is 100% effective at preventing infection, also they have varying efficacy rates and different side effects. However, the main protease (Mpro) of SARS-CoV-2 has been identified as a key drug target due to its essential role in viral infection and its minimal similarity with human proteases. Cordyceps mushrooms have been found to have various therapeutic properties that could effectively combat SARS-CoV-2, including improve lung functioning, anti-viral, immunomodulators, anti-infectious, and anti-inflammatory. The present study aims to screen and evaluate the inhibitory potential of the bioactive molecules from the Cordyceps species against the Mpro of SARS-CoV-2. The bioactive molecules were screened based on their docking score, molecular interactions in the binding pocket, ADME properties, toxicity, carcinogenicity, and mutagenicity. Among all the molecules that were tested, cordycepic acid was the most effective and promising candidate, with a binding affinity of -8.10 kcal/mol against Mpro. The molecular dynamics (MD) simulation and free binding energy calculations revealed that the cordycepic acid-Mpro complex was highly stable and showed fewer conformational fluctuations. These findings need to be investigated further through in-vitro and in-vivo studies for additional validation.Communicated by Ramaswamy H. Sarma.

Upgrade of crystallography beamline BL19U1 at the Shanghai Synchrotron Radiation Facility

BL19U1, an energy-tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure-based drug design utilizing X-ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low-temperature or room-temperature data collection modes. The control system has been upgraded from Blu-Ice to MXCuBE3, which supports website-mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real-time feedback on data quality.

Emerging and Re-emerging Warheads for Targeted Covalent Inhibitors: An Update

Covalent inhibitors and other types of covalent modalities have seen a revival in the past two decades, with a variety of new targeted covalent drugs having been approved in recent years. A key feature of such molecules is an intrinsically reactive group, typically a weak electrophile, which enables the irreversible or reversible formation of a covalent bond with a specific amino acid of the target protein. This reactive group, often called the "warhead", is a critical determinant of the ligand's activity, selectivity, and general biological properties. In 2019, we summarized emerging and re-emerging warhead chemistries to target cysteine and other amino acids (Gehringer, M.; Laufer, S. A. J. Med. Chem. 2019, 62, 5673-5724; DOI: 10.1021/acs.jmedchem.8b01153). Since then, the field has rapidly evolved. Here we discuss the progress on covalent warheads made since our last Perspective and their application in medicinal chemistry and chemical biology.

SARS-CoV-2 3CLPro Dihedral Angles Reveal Allosteric Signaling

In allosteric proteins, identifying the pathways that signals take from allosteric ligand-binding sites to enzyme active sites or binding pockets and interfaces remains challenging. This avenue of research is motivated by the goals of understanding particular macromolecular systems of interest and creating general methods for their study. An especially important protein that is the subject of many investigations in allostery is the SARS-CoV-2 main protease (Mpro), which is necessary for coronaviral replication. It is both an attractive drug target and, due to intense interest in it for the development of pharmaceutical compounds, a gauge of the state-of-the-art approaches in studying protein inhibition. Here we develop a computational method for characterizing protein allostery and use it to study Mpro. We propose a role of the protein's C-terminal tail in allosteric modulation and warn of unintuitive traps that can plague studies of the role of protein dihedrals angles in transmitting allosteric signals.

Cyclic β2,3-amino acids improve the serum stability of macrocyclic peptide inhibitors targeting the SARS-CoV-2 main protease

Due to their constrained conformations, cyclic β2,3-amino acids (cβAA) are key building blocks that can fold peptides into compact and rigid structures, improving peptidase resistance and binding affinity to target proteins, due to their constrained conformations. Although the translation efficiency of cβAAs is generally low, our engineered tRNA, referred to as tRNAPro1E2, enabled efficient incorporation of cβAAs into peptide libraries using the flexible in vitro translation (FIT) system. Here we report on the design and application of a macrocyclic peptide library incorporating 3 kinds of cβAAs: (1R,2S)-2-aminocyclopentane carboxylic acid (β1), (1S,2S)-2-aminocyclohexane carboxylic acid (β2), and (1R,2R)-2-aminocyclopentane carboxylic acid. This library was applied to an in vitro selection against the SARS-CoV-2 main protease (Mpro). The resultant peptides, BM3 and BM7, bearing one β2 and two β1, exhibited potent inhibitory activities with IC50 values of 40 and 20 nM, respectively. BM3 and BM7 also showed remarkable serum stability with half-lives of 48 and >168 h, respectively. Notably, BM3A and BM7A, wherein the cβAAs were substituted with alanine, lost their inhibitory activities against Mpro and displayed substantially shorter serum half-lives. This observation underscores the significant contribution of cβAA to the activity and stability of peptides. Overall, our results highlight the potential of cβAA in generating potent and highly stable macrocyclic peptides with drug-like properties.

SARS-CoV-2 Mpro oligomerization as a potential target for therapy

The main protease (Mpro) of SARS-CoV-2 is critical in the virus's replication cycle, facilitating the maturation of polyproteins into functional units. Due to its conservation across taxa, Mpro is a promising target for broad-spectrum antiviral drugs. Targeting Mpro with small molecule inhibitors, such as nirmatrelvir combined with ritonavir (Paxlovid™), which the FDA has approved for post-exposure treatment and prophylaxis, can effectively interrupt the replication process of the virus. A key aspect of Mpro's function is its ability to form a functional dimer. However, the mechanics of dimerization and its influence on proteolytic activity remain less understood. In this study, we utilized biochemical, structural, and molecular modelling approaches to explore Mpro dimerization. We evaluated critical residues, specifically Arg4 and Arg298, that are essential for dimerization. Our results show that changes in the oligomerization state of Mpro directly affect its enzymatic activity and dimerization propensity. We discovered a synergistic relationship influencing dimer formation, involving both intra- and intermolecular interactions. These findings highlight the potential for developing allosteric inhibitors targeting Mpro, offering promising new directions for therapeutic strategies.

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

A Rationally Designed Synthetic Antiviral Peptide Binder Targeting the Receptor-Binding Domain of SARS-CoV-2

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus, is the causative agent responsible for the spread of the COVID19 pandemic across the globe. The global impact of the COVID19 pandemic, the successful approval of vaccines for controlling the pandemic, and the further resurgence of COVID19 necessitate the exploration and validation of alternative therapeutic avenues targeting SARS-CoV-2. The initial entry and further invasion by SARS-CoV-2 require strong protein-protein interactions (PPIs) between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptors expressed on the cell surfaces of various tissues. In principle, disruption of the PPIs between the RBD of SARS-CoV-2 and the ACE2 receptor by designer peptides with optimized pharmacology appears to be an ideal choice for potentially preventing viral entry with minimal immunogenicity. In this context, the current study describes a short, synthetic designer peptide (codenamed SR16, ≤18 aa, molecular weight ≤2.5 kDa), which has a few noncoded amino acids, demonstrates a helical conformation in solution, and also engages the RBD of SARS-CoV-2 through a high-affinity interaction, as judged from a battery of biophysical studies. Further, the designer peptide demonstrates resistance to trypsin degradation, appears to be nontoxic to mammalian cells, and also does not induce hemolysis in freshly isolated human erythrocytes. In summary, SR16 appears to be an ideal peptide binder targeting the RBD of SARS-CoV-2, which has the potential for further optimization and development as an antiviral agent targeting SARS-CoV-2.

Quantitative Assessment of Energetic Contributions of Residues in a SARS-CoV-2 Viral Enzyme/Nanobody Interface

The highly conserved protease enzyme from SARS-CoV-2 (MPro) is crucial for viral replication and is an attractive target for the design of novel inhibitory compounds. MPro is known to be conformationally flexible and has been stabilized in an extended conformation in a complex with a novel nanobody (NB2B4), which inhibits the dimerization of the enzyme via binding to an allosteric site. However, the energetic contributions of the nanobody residues stabilizing the MPro/nanobody interface remain unresolved. We probed these residues using all-atom MD simulations in combination with alchemical free energy calculations by studying the physical residue-residue interactions and discovered the role of hydrophobic and electrostatic interactions in stabilizing the complex. Specifically, we found via mutational analysis that three interfacial nanobody residues (Y59, R106, and L109) contributed significantly, two residues (L107 and P110) contributed moderately, and two residues (H112 and T113) contributed minimally to the overall binding affinity of the nanobody. We also discovered that the nanobody affinity could be enhanced via a charge-reversal mutation (D62R) that alters the local interfacial electrostatic environment of this residue in the complex. These findings are potentially useful in designing novel synthetic nanobodies as allosteric inhibitors of MPro.

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Inhibitory effects against SARSCoV-2 main protease (Mpro) of biflavonoids and benzophenones from the fruit of Platonia insignis

The SARS-CoV-2 mutation and the limitation of the approved drug against COVID-19 are still a challenge in many country healthcare systems and need to be affronted despite the set of vaccines to prevent this viral infection. To contribute to the identification of new antiviral agents, the present study focused on natural products from an edible fruit with potential inhibitory effects against the SARS-CoV-2 main protease (Mpro). First, LC-ESIMS analysis of Platonia insignis fruits was performed and showed the presence of biflavonoids and benzophenones in the seed and pulp, respectively. Then, maceration and chromatographic purification led to the identification of two triglycerides (1 and 2) alongside chamaejasmine (3) and volkensiflavone (4) from the seed and isogarcinol (5) and cycloxanthochymol (6), from the pulp. Compounds 1-6 after evaluating their inhibitory against Mpro, displayed from no to significant activity. Compound 5 was the most potent with an IC50 value of 0.72 μM and was more active than the positive control, Ebselen (IC50 of 3.4 μM). It displayed weak and no cytotoxicity against THP-1 (CC50 of 116.2 μM) and Vero cell lines, respectively. Other active compounds showed no cytotoxicity against THP-1. and Vero cell lines. Molecular docking studies revealed interactions in the catalytic pocket between compound 5 and amino acid residues that composed the catalytic dyads (His 41 and Cyst 145).

Recent Advances on Targeting Proteases for Antiviral Development

Viral proteases are an important target for drug development, since they can modulate vital pathways in viral replication, maturation, assembly and cell entry. With the (re)appearance of several new viruses responsible for causing diseases in humans, like the West Nile virus (WNV) and the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), understanding the mechanisms behind blocking viral protease's function is pivotal for the development of new antiviral drugs and therapeutical strategies. Apart from directly inhibiting the target protease, usually by targeting its active site, several new pathways have been explored to impair its activity, such as inducing protein aggregation, targeting allosteric sites or by inducing protein degradation by cellular proteasomes, which can be extremely valuable when considering the emerging drug-resistant strains. In this review, we aim to discuss the recent advances on a broad range of viral proteases inhibitors, therapies and molecular approaches for protein inactivation or degradation, giving an insight on different possible strategies against this important class of antiviral target.

Targeting the Main Protease (Mpro, nsp5) by Growth of Fragment Scaffolds Exploiting Structure-Based Methodologies

The main protease Mpro, nsp5, of SARS-CoV-2 (SCoV2) is one of its most attractive drug targets. Here, we report primary screening data using nuclear magnetic resonance spectroscopy (NMR) of four different libraries and detailed follow-up synthesis on the promising uracil-containing fragment Z604 derived from these libraries. Z604 shows time-dependent binding. Its inhibitory effect is sensitive to reducing conditions. Starting with Z604, we synthesized and characterized 13 compounds designed by fragment growth strategies. Each compound was characterized by NMR and/or activity assays to investigate their interaction with Mpro. These investigations resulted in the four-armed compound 35b that binds directly to Mpro. 35b could be cocrystallized with Mpro revealing its noncovalent binding mode, which fills all four active site subpockets. Herein, we describe the NMR-derived fragment-to-hit pipeline and its application for the development of promising starting points for inhibitors of the main protease of SCoV2.

The SARS-CoV-2 Mpro Dimer-Based Screening System: A Synthetic Biology Tool for Identifying Compounds with Dimerization Inhibitory Potential

The COVID-19 endemic remains a global concern. The search for effective antiviral candidates is still needed to reduce disease risk. However, the availability of high biosafety level laboratory facilities for drug screening is limited in number. To address this issue, a screening system that could be utilized at lower biosafety levels remains essential. This study aimed to develop a novel SARS-CoV-2 main protease (Mpro) dimer-based screening system (DBSS) utilizing synthetic biology in Escherichia coli BL21(DE3). We linked the SARS-CoV-2 Mpro with the DNA-binding domain of AraC regulatory protein, which regulates the reporter gene expression. Protein modeling and molecular docking showed that saquinavir could bind to AraC-Mpro both in its monomer and dimer forms. The constructed DBSS assay indicated the screening system could detect saquinavir inhibitory activity at a concentration range of 4-10 μg/mL compared to the untreated control (P ≤ 0.05). The Vero E6 cell assay validated the DBSS result that saquinavir at 4-10 μg/mL exhibited antiviral activity against SARS-CoV-2. Our DBSS could be used for preliminary screening of numerous drug candidates that possess a dimerization inhibitor activity of SARS-CoV-2 Mpro and also minimize the use of a high biosafety level laboratory.

Fragment-based screening targeting an open form of the SARS-CoV-2 main protease binding pocket

To identify starting points for therapeutics targeting SARS-CoV-2, the Paul Scherrer Institute and Idorsia decided to collaboratively perform an X-ray crystallographic fragment screen against its main protease. Fragment-based screening was carried out using crystals with a pronounced open conformation of the substrate-binding pocket. Of 631 soaked fragments, a total of 29 hits bound either in the active site (24 hits), a remote binding pocket (three hits) or at crystal-packing interfaces (two hits). Notably, two fragments with a pose that was sterically incompatible with a more occluded crystal form were identified. Two isatin-based electrophilic fragments bound covalently to the catalytic cysteine residue. The structures also revealed a surprisingly strong influence of the crystal form on the binding pose of three published fragments used as positive controls, with implications for fragment screening by crystallography.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Campesterol and dithymoquinone as a potent inhibitors of SARS cov-2 main proteases-promising drug candidates for targeting its novel variants

The sudden outbreak of the COVID-19 pandemic has currently taken approximately 2.4 million lives, with no specific medication and fast-tracked tested vaccines for prevention. These vaccines have their own adverse effects, which have severely affected the global healthcare system. The discovery of the main protease structure of coronavirus (Mpro/Clpro) has resulted in the identification of compounds having antiviral potential, especially from the herbal system. In this study, the computer-associated drug design tools were utilised to analyze the reported phytoconstituents of Nigella sativa for their antiviral activity against the main protease. Fifty-eight compounds were subjected to pharmacological parameter analysis to determine their lead likeness in comparison to the standard drugs (chloroquine and nirmatrelvir) used in the treatment of SARS-CoV-2. Nearly 31 compounds were docked against five different SARS-CoV-2 main proteases, and all compounds showed better binding affinity and inhibition constant against the proteases. However, dithymoquinone and campesterol displayed the best binding scores and hence were further subjected to dynamics and MMPBSA study for 100 ns. The stability analysis shows that dithymoquinone and campesterol show less variation in fluctuation in residues compared to standard complexes. Moreover, dithymoquinone exhibited higher binding affinity and favorable interaction followed by campesterol as compared to the standard drug. The in silico computational analysis provides a promising hit for regulating the main proteases activity.Communicated by Ramaswamy H. Sarma.

NMR 1H,19F-based screening of the four stem-looped structure 5_SL1-SL4 located in the 5'-untranslated region of SARS-CoV 2 RNA

Development of new antiviral medication against the beta-coronavirus SARS-CoV-2 (SCoV2) is actively being pursued. Both NMR spectroscopy and crystallography as structural screening technologies have been utilised to screen the viral proteome for binding to fragment libraries. Here, we report on NMR screening of elements of the viral RNA genome with two different ligand libraries using 1H-NMR-screening experiments and 1H and 19F NMR-screening experiments for fluorinated compounds. We screened against the 5'-terminal 119 nucleotides located in the 5'-untranslated region of the RNA genome of SCoV2 and further dissected the four stem-loops into its constituent RNA elements to test specificity of binding of ligands to shorter and longer viral RNA stretches. The first library (DRTL-F library) is enriched in ligands binding to RNA motifs, while the second library (DSI-poised library) represents a fragment library originally designed for protein screening. Conducting screens with two different libraries allows us to compare different NMR screening methodologies, describe NMR screening workflows, validate the two different fragment libraries, and derive initial leads for further downstream medicinal chemistry optimisation.

On the origins of SARS-CoV-2 main protease inhibitors

In order to address the world-wide health challenge caused by the COVID-19 pandemic, the 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-Mpro) coded by its nsp5 gene became one of the biochemical targets for the design of antiviral drugs. In less than 3 years of research, 4 inhibitors of SARS-CoV-2-Mpro have actually been authorized for COVID-19 treatment (nirmatrelvir, ensitrelvir, leritrelvir and simnotrelvir) and more such as EDP-235, FB-2001 and STI-1558/Olgotrelvir or five undisclosed compounds (CDI-988, ASC11, ALG-097558, QLS1128 and H-10517) are undergoing clinical trials. This review is an attempt to picture this quite unprecedented medicinal chemistry feat and provide insights on how these cysteine protease inhibitors were discovered. Since many series of covalent SARS-CoV-2-Mpro inhibitors owe some of their origins to previous work on other proteases, we first provided a description of various inhibitors of cysteine-bearing human caspase-1 or cathepsin K, as well as inhibitors of serine proteases such as human dipeptidyl peptidase-4 or the hepatitis C protein complex NS3/4A. This is then followed by a description of the results of the approaches adopted (repurposing, structure-based and high throughput screening) to discover coronavirus main protease inhibitors.

Electrophilic MiniFrags Revealed Unprecedented Binding Sites for Covalent HDAC8 Inhibitors

Screening of ultra-low-molecular weight ligands (MiniFrags) successfully identified viable chemical starting points for a variety of drug targets. Here we report the electrophilic analogues of MiniFrags that allow the mapping of potential binding sites for covalent inhibitors by biochemical screening and mass spectrometry. Small electrophilic heterocycles and their N-quaternized analogues were first characterized in the glutathione assay to analyze their electrophilic reactivity. Next, the library was used for systematic mapping of potential covalent binding sites available in human histone deacetylase 8 (HDAC8). The covalent labeling of HDAC8 cysteines has been proven by tandem mass spectrometry measurements, and the observations were explained by mutating HDAC8 cysteines. As a result, screening of electrophilic MiniFrags identified three potential binding sites suitable for the development of allosteric covalent HDAC8 inhibitors. One of the hit fragments was merged with a known HDAC8 inhibitor fragment using different linkers, and the linker length was optimized to result in a lead-like covalent inhibitor.

Multiple redox switches of the SARS-CoV-2 main protease in vitro provide opportunities for drug design

Besides vaccines, the development of antiviral drugs targeting SARS-CoV-2 is critical for preventing future COVID outbreaks. The SARS-CoV-2 main protease (Mpro), a cysteine protease with essential functions in viral replication, has been validated as an effective drug target. Here, we show that Mpro is subject to redox regulation in vitro and reversibly switches between the enzymatically active dimer and the functionally dormant monomer through redox modifications of cysteine residues. These include a disulfide-dithiol switch between the catalytic cysteine C145 and cysteine C117, and generation of an allosteric cysteine-lysine-cysteine SONOS bridge that is required for structural stability under oxidative stress conditions, such as those exerted by the innate immune system. We identify homo- and heterobifunctional reagents that mimic the redox switching and inhibit Mpro activity. The discovered redox switches are conserved in main proteases from other coronaviruses, e.g. MERS-CoV and SARS-CoV, indicating their potential as common druggable sites.

An expanded view of ligandability in the allosteric enzyme PTP1B from computational reanalysis of large-scale crystallographic data

The recent advent of crystallographic small-molecule fragment screening presents the opportunity to obtain unprecedented numbers of ligand-bound protein crystal structures from a single high-throughput experiment, mapping ligandability across protein surfaces and identifying useful chemical footholds for structure-based drug design. However, due to the low binding affinities of most fragments, detecting bound fragments from crystallographic datasets has been a challenge. Here we report a trove of 65 new fragment hits across 59 new liganded crystal structures for PTP1B, an "undruggable" therapeutic target enzyme for diabetes and cancer. These structures were obtained from computational analysis of data from a large crystallographic screen, demonstrating the power of this approach to elucidate many (~50% more) "hidden" ligand-bound states of proteins. Our new structures include a fragment hit found in a novel binding site in PTP1B with a unique location relative to the active site, one that validates another new binding site recently identified by simulations, one that links adjacent allosteric sites, and, perhaps most strikingly, a fragment that induces long-range allosteric protein conformational responses via a previously unreported intramolecular conduit. Altogether, our research highlights the utility of computational analysis of crystallographic data, makes publicly available dozens of new ligand-bound structures of a high-value drug target, and identifies novel aspects of ligandability and allostery in PTP1B.

Discovery of proteolysis-targeting chimera targeting undruggable proteins using a covalent ligand screening approach

Targeted protein degradation (TPD) technology, such as proteolysis-targeting chimera (PROTAC), has become a new therapeutic modality. However, the degradation of undruggable proteins, such as those involved in protein-protein interactions (PPIs), using PROTAC is still limited owing to the difficulties in finding small-molecule binders of these proteins. To identify new chemical moieties that bind to the target sites of the protein of interest (POI), we conducted a site-specific and fragment-based covalent ligand screening using liquid chromatography-tandem mass spectrometry (LC-MS/MS). To apply the selected hits to the PROTAC approach, two-dimensional (2D) nuclear magnetic resonance (NMR) experiments were performed to evaluate the reversible binding of their analogs without covalent warheads. To proof the proposed approach, human mouse double minute (MDM)2 was selected as a model system since it is involved in PPIs and is known to be a degradable target protein. Western blot analysis showed that newly synthesized PROTACs, incorporated reversible analogs of screening hits, affected degradation in a dose- and time-dependent manner. This methodology makes it possible to use PROTAC technology to exploit previously undruggable proteins for TPD.

Molecular Mechanism of Labelling Functional Cysteines by Heterocyclic Thiones

Heterocyclic thiones have recently been identified as reversible covalent warheads, consistent with their mild electrophilic nature. Little is known so far about their mechanism of action in labelling nucleophilic sidechains, especially cysteines. The vast number of tractable cysteines promotes a wide range of target proteins to examine; however, our focus was put on functional cysteines. We chose the main protease of SARS-CoV-2 harboring Cys145 at the active site that is a structurally characterized and clinically validated target of covalent inhibitors. We screened an in-house, cysteine-targeting covalent inhibitor library which resulted in several covalent fragment hits with benzoxazole, benzothiazole and benzimidazole cores. Thione derivatives and Michael acceptors were selected for further investigations with the objective of exploring the mechanism of inhibition of the thiones and using the thoroughly characterized Michael acceptors for benchmarking our studies. Classical and hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations were carried out that revealed a new mechanism of covalent cysteine labelling by thione derivatives, which was supported by QM and free energy calculations and by a wide range of experimental results. Our study shows that the molecular recognition step plays a crucial role in the overall binding of both sets of molecules.

Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 Mpro inhibitors

The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.

A community effort in SARS-CoV-2 drug discovery

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

Structural Analyses of Bacterial Effectors by X-Ray Crystallography

X-ray crystallography is a method of choice to determine and analyze protein structures. Although large complexes are challenging to crystallize and cryo-electron microscopy is thus better suited for these, crystallography can still be efficient in solving structures of single components of secretion systems or effectors. Many of the different steps leading to structure determination by X-ray crystallography have been automatized. Here, we describe a generic approach to obtain crystals, solve the structure of a given protein, and perform a preliminary analysis, highlighting novel and efficient possibilities offered by automatization and contribution of Alpha Fold 2 structure prediction.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

Identification of Ebselen derivatives as novel SARS-CoV-2 main protease inhibitors: Design, synthesis, biological evaluation, and structure-activity relationships exploration

The main protease (Mpro) represents one of the most effective and attractive targets for designing anti-SARS-CoV-2 drugs. In this study, we designed and synthesized a novel series of Ebselen derivatives by incorporating privileged fragments from different pockets of the Mpro active site. Among these compounds, 11 compounds showed submicromolar activity in the FRET-based SARS-CoV-2 Mpro inhibition assay, with IC50 values ranging from 233 nM to 550 nM. Notably, compound 3a displayed submicromolar Mpro activity (IC50 = 364 nM) and low micromolar antiviral activity (EC50 = 8.01 µM), comparable to that of Ebselen (IC50 = 339 nM, EC50 = 3.78 µM). Time-dependent inhibition assay confirmed that these compounds acted as covalent inhibitors. Taken together, our optimization campaigns thoroughly explored the structural diversity of Ebselen and verified the impact of specific modifications on potency against Mpro.

A novel class of broad-spectrum active-site-directed 3C-like protease inhibitors with nanomolar antiviral activity against highly immune-evasive SARS-CoV-2 Omicron subvariants

Antivirals with broad coronavirus activity are important for treating high-risk individuals exposed to the constantly evolving SARS-CoV-2 variants of concern (VOCs) as well as emerging drug-resistant variants. We developed and characterized a novel class of active-site-directed 3-chymotrypsin-like protease (3CLpro) inhibitors (C2-C5a). Our lead direct-acting antiviral (DAA), C5a, is a non-covalent, non-peptide with a dissociation constant of 170 nM against recombinant SARS-CoV-2 3CLpro. The compounds C2-C5a exhibit broad-spectrum activity against Omicron subvariants (BA.5, BQ.1.1, and XBB.1.5) and seasonal human coronavirus-229E infection in human cells. Notably, C5a has median effective concentrations of 30-50 nM against BQ.1.1 and XBB.1.5 in two different human cell lines. X-ray crystallography has confirmed the unique binding modes of C2-C5a to the 3CLpro, which can limit virus cross-resistance to emerging Paxlovid-resistant variants. We tested the effect of C5a with two of our newly discovered host-directed antivirals (HDAs): N-0385, a TMPRSS2 inhibitor, and bafilomycin D (BafD), a human vacuolar H+-ATPase [V-ATPase] inhibitor. We demonstrated a synergistic action of C5a in combination with N-0385 and BafD against Omicron BA.5 infection in human Calu-3 lung cells. Our findings underscore that a SARS-CoV-2 multi-targeted treatment for circulating Omicron subvariants based on DAAs (C5a) and HDAs (N-0385 or BafD) can lead to therapeutic benefits by enhancing treatment efficacy. Furthermore, the high-resolution structures of SARS-CoV-2 3CLpro in complex with C2-C5a will facilitate future rational optimization of our novel broad-spectrum active-site-directed 3C-like protease inhibitors.

A simple method for developing lysine targeted covalent protein reagents

Peptide-based covalent probes can target shallow protein surfaces not typically addressable using small molecules, yet there is a need for versatile approaches to convert native peptide sequences into covalent binders that can target a broad range of residues. Here we report protein-based thio-methacrylate esters-electrophiles that can be installed easily on unprotected peptides and proteins via cysteine side chains, and react efficiently and selectively with cysteine and lysine side chains on the target. Methacrylate phosphopeptides derived from 14-3-3-binding proteins irreversibly label 14-3-3σ via either lysine or cysteine residues, depending on the position of the electrophile. Methacrylate peptides targeting a conserved lysine residue exhibit pan-isoform binding of 14-3-3 proteins both in lysates and in extracellular media. Finally, we apply this approach to develop protein-based covalent binders. A methacrylate-modified variant of the colicin E9 immunity protein irreversibly binds to the E9 DNAse, resulting in significantly higher thermal stability relative to the non-covalent complex. Our approach offers a simple and versatile route to convert peptides and proteins into potent covalent binders.

Novel pseudonucleosides and sulfamoyl-oxazolidinone β-D-glucosamine derivative as anti-COVID-19: design, synthesis, and in silico study

New pseudonucleosides containing cyclic sulfamide moiety and sulfamoyl β-D-glucosamine derivative are described. These pseudonucleosides are synthesized in good yields starting from chlorosulfonyl isocyanate and β-D-glucosamine hydrochloride in five steps; (protection, acetylation, removal of the Boc group, sulfamoylation, and cyclization). Further, novel glycosylated sulfamoyloxazolidin-2-one is prepared in three steps; carbamoylation, sulfamoylation, and intramolecular cyclization. The structures of the synthesized compounds were confirmed by usual spectroscopic and spectrometric methods NMR, IR, MS, and EA. Interesting molecular docking of the prepared pseudonucleosides and (Beclabuvir, Remdesivir) drugs with SARS-CoV-2/Mpro (PDB:5R80) was conducted using the same parameters for a fair comparison. A low binding affinity of the synthesized compounds compared to the Beclabuvir and other analysis showed that pseudonucleosides have the ability to inhibit SARS-CoV-2. After the motivating results of molecular docking study, the complex between the SARS-CoV-2 Mpro and compound 7 was subjected to 100 ns molecular dynamics (MD) simulation using Desmond module of Schrodinger suite, during which the receptor-ligand complex showed substantial stability after 10 ns of MD simulation. Also, we studied the prediction of absorption, distribution, properties of metabolism, excretion, and toxicity (ADMET) of the synthesized compounds.Communicated by Ramaswamy H. Sarma.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Preparing for the next pandemic

New lead drugs to treat COVID-19 are beginning to emerge.