Systematic Analyses of the Resistance Potential of Drugs Targeting SARS-CoV-2 Main Protease

Enhancing the understandings on SARS-CoV-2 main protease (Mpro) mutants from molecular dynamics and machine learning

While star drugs like Paxlovid have shown remarkable performance in combating SARS-CoV-2, we still face serious challenges such as viral mutants and resistance. In this study, we employ a computational framework combining molecular dynamics (MD) simulations, enhanced sampling techniques, and machine learning (ML) approaches to systematically investigate the molecular mechanisms underlying drug resistance in SARS-CoV-2 main protease (Mpro) mutants. Specifically, based on the accuracy of the analytical structures and the advantages of MD simulation, we deeply analyze the influence of mutants on drug resistance and its intrinsic function from the dynamic dimension. The relevant data for Mpro with different states are compared and analyzed to consolidate the understanding of mutant effect. Through the free energy perturbation method, the absolute binding free energy diagrams of Mpro mutants and Nirmatrelvir are provided, which is meaningful to the design, comparison and optimization of the new-generation inhibitors. The interaction pattern between Mpro mutants and substrate is unraveled with the AlphaFold3 model, effectively filling the deficiency of experiments. Moreover, ML model is used to explore the differentiated synergetic pathways with the important dual mutants. The critical sites in the protein network are provided, which emphasizes on the importance and urgency of in-depth research on similar systems.

M49L and other drug resistance mutations emerging in individuals after administration of ensitrelvir in Japanese clinical settings

Recent in-vitro and in-vivo studies and analysis of genomic information registered in GISAID have raised concerns about drug resistance mutations such as M49L after treatment with the 3C-like protease inhibitor ensitrelvir. The aim of this study was to identify resistance gene mutations to 3C-like protease inhibitors, including M49L mutations, in Japanese clinical settings. Genomic analysis of samples from our hospital admissions showed M49L mutations associated with ensitrelvir treatment in three cases and M49L mutation unrelated to ensitrelvir treatment in three cases. In a study of cases with persistent infection or rebound in viral load after 5 days of ensitrelvir treatment, 10 of 16 patients had M49L mutations and 5 had M49I mutations. Resistance gene mutations following treatment with ensitrelvir were shown to emerge even within individual patients who were not immunocompromised. In a study of persistent SARS-CoV-2 infection in severely immunocompromised patients, various drug resistance mutations emerged, with the M49L mutation especially showing a tendency to be a majority mutation. The current status of drug resistance mutations occurring in individuals following administration of ensitrelvir in Japanese clinical settings was clinically investigated for the first time. Considering that the barrier to resistance to ensitrelvir is lower than that to other antiviral drugs and that M49L is a unique mutation that occurs quickly, tends to become a majority mutation, and is maintained thereafter through its ability to replicate, the spread of strains that have acquired ensitrelvir resistance should be closely monitored.

Targeting Matrix Metalloproteinase-1, Matrix Metalloproteinase-7, and Serine Protease Inhibitor E1: Implications in preserving lung vascular endothelial integrity and immune modulation in COVID-19

BACKGROUND

SARS-CoV-2 disrupts lung vascular endothelial integrity, contributing to severe COVID-19 complications. However, the molecular mechanisms driving endothelial dysfunction remain underexplored, and targeted therapeutic strategies are lacking.

OBJECTIVE

This study investigates Naringenin-7-O-glucoside (N7G) as a multi-target therapeutic candidate for modulating vascular integrity and immune response by inhibiting MMP1, MMP7, and SERPINE1-key regulators of extracellular matrix (ECM) remodeling and inflammation.

METHODS & RESULTS

RNA-seq analysis of COVID-19 lung tissues identified 17 upregulated N7G targets, including MMP1, MMP7, and SERPINE1, with the latter exhibiting the highest expression. PPI network analysis linked these targets to ECM degradation, IL-17, HIF-1, and AGE-RAGE signaling pathways, and endothelial dysfunction. Disease enrichment associated these genes with idiopathic pulmonary fibrosis and asthma. Molecular docking, 200 ns MD simulations (triplicate), and MMGBSA calculations confirmed N7G's stable binding affinity to MMP1, MMP7, and SERPINE1. Immune profiling revealed increased neutrophils and activated CD4+ T cells, alongside reduced mast cells, NK cells, and naïve B cells, indicating immune dysregulation. Correlation analysis linked MMP1, MMP7, and SERPINE1 to distinct immune cell populations, supporting N7G's immunomodulatory role.

CONCLUSION

These findings suggest that N7G exhibits multi-target therapeutic potential by modulating vascular integrity, ECM remodeling, and immune dysregulation, positioning it as a promising candidate for mitigating COVID-19-associated endothelial dysfunction.

Potency Prediction of Covalent Inhibitors against SARS-CoV-2 3CL-like Protease and Multiple Mutants by Multiscale Simulations

3-Chymotrypsin-like protease (3CLpro) is a prominent target against pathogenic coronaviruses. Expert knowledge of the cysteine-targeted covalent reaction mechanism is crucial to predict the inhibitory potency of approved inhibitors against 3CLpros of SARS-CoV-2 variants and perform structure-based drug design against newly emerging coronaviruses. We carried out an extensive array of classical and hybrid QM/MM molecular dynamics simulations to explore covalent inhibition mechanisms of five well-characterized inhibitors toward SARS-CoV-2 3CLpro and its mutants. The calculated binding affinity and reactivity of the inhibitors are highly consistent with experimental data, and the predicted inhibitory potency of the inhibitors against 3CLpro with L167F, E166V, or T21I/E166V mutant is in full agreement with IC50s determined by the accompanying enzymatic assays. The explored mechanisms unveil the impact of residue mutagenesis on structural dynamics that communicates to change not only noncovalent binding strength but also covalent reaction free energy. Such a change is inhibitor dependent, corresponding to varied levels of drug resistance of these 3CLpro mutants against nirmatrelvir and simnotrelvir and no resistance to the 11a compound. These results together suggest that the present simulations with a suitable protocol can efficiently evaluate the reactivity and potency of covalent inhibitors along with the elucidated molecular mechanisms of covalent inhibition.

An Investigation of Nirmatrelvir (Paxlovid) Resistance in SARS-CoV-2 Mpro

The high throughput YESS 2.0 platform was used to screen a large library of SARS-CoV-2 Mpro variants in the presence of nirmatrelvir. Of the 100 individual most prevalent mutations identified in the screen and reported here, the most common were E166V, L27V, N142S, A173V, and Y154N, along with their various combinations. In vitro analysis revealed that resistance to nirmatrelvir for these individual mutations, as well as all of the combinations we analyzed, was accompanied by decreased catalytic activity with the native substrate. Importantly, the mutations we identified have not appeared as significantly enriched in SARS-CoV-2 Mpro sequences isolated from COVID-19 patients following the introduction of nirmatrelvir. We also analyzed three of the most common SARS-CoV-2 Mpro mutations that have been seen in patients recently, and only a measured increase in nirmatrelvir resistance was seen when the more recently appearing A285V is added to both P132H and K90R. Taken together, our results predict that resistance to nirmatrelvir will be slower to develop than expected based on experience with other viral protease inhibitors, perhaps due in part to the close structural correspondence between nirmatrelvir and SARS-CoV-2 Mpro's preferred substrates.

Synthesis and evaluation of N-arylindazole-3-carboxamide derivatives as novel antiviral agents against SARS-CoV-2

N-Arylindazole-3-carboxamide derivatives synthesized from an anti-MERS-CoV hit compound showed potent inhibitory activities against SARS-CoV-2. Among them, 5-chloro-N-(3,5-dichlorophenyl)-1H-indazole-3-carboxamide (4a) exhibited a potent inhibitory effect (EC50 = 0.69 µM), low cytotoxicity, and satisfactory in vitro PK profiles. Thus, N-arylindazole-3-carboxamide 4a provides a novel template for future development of anti-coronavirus agents.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

Backstage Heroes-Yeast in COVID-19 Research

The extremely rapid development of understanding and technology that led to the containment of the COVID-19 pandemic resulted from collaborative efforts in the fields of Betacoronavirus pandemicum (SARS-CoV-2) biology, pharmacology, vaccinology, and medicine. Perhaps surprisingly, much of the research was conducted using simple and efficient yeast models. In this manuscript, we describe how yeast, eukaryotic microorganisms, have been used to research this global challenge, focusing on the therapeutic potential of the studies discussed herein. Thus, we outline the role of yeast in studying viral protein interactions with the host cell proteome, including the binding of the SARS-CoV-2 virus spike protein to the human ACE2 receptor and its modulation. The production and exploration of viral antigens in yeast systems, which led to the development of two approved COVID-19 vaccines, are also detailed. Moreover, yeast platforms facilitating the discovery and production of single-domain antibodies (nanobodies) against SARS-CoV-2 are described. Methods guiding modern and efficient drug discovery are explained at length. In particular, we focus on studies designed to search for inhibitors of the main protease (Mpro), a unique target for anti-coronaviral therapies. We highlight the adaptability of the techniques used, providing opportunities for rapid modification and implementation alongside the evolution of the SARS-CoV-2 virus. Approaches introduced in yeast systems that may have universal potential application in studies of emerging viral diseases are also described.

Identification of Potent, Broad-Spectrum Coronavirus Main Protease Inhibitors for Pandemic Preparedness

The COVID-19 pandemic highlights the ongoing risk of zoonotic transmission of coronaviruses to global health. To prepare for future pandemics, it is essential to develop effective antivirals targeting a broad range of coronaviruses. Targeting the essential and clinically validated coronavirus main protease (Mpro), we constructed a structurally diverse Mpro panel by clustering all known coronavirus sequences by Mpro active site sequence similarity. Through screening, we identified a potent covalent inhibitor that engaged the catalytic cysteine of SARS-CoV-2 Mpro and used structure-based medicinal chemistry to develop compounds in the pyrazolopyrimidine sulfone series that exhibit submicromolar activity against multiple Mpro homologues. Additionally, we solved the first X-ray cocrystal structure of Mpro from the human-infecting OC43 coronavirus, providing insights into potency differences among compound-target pairs. Overall, the chemical compounds described in this study serve as starting points for the development of antivirals with broad-spectrum activity, enhancing our preparedness for emerging human-infecting coronaviruses.

The small molecule inhibitor of SARS-CoV-2 3CLpro EDP-235 prevents viral replication and transmission in vivo

The COVID-19 pandemic has led to the deaths of millions of people and severe global economic impacts. Small molecule therapeutics have played an important role in the fight against SARS-CoV-2, the virus responsible for COVID-19, but their efficacy has been limited in scope and availability, with many people unable to access their benefits, and better options are needed. EDP-235 is specifically designed to inhibit the SARS-CoV-2 3CLpro, with potent nanomolar activity against all SARS-CoV-2 variants to date, as well as clinically relevant human and zoonotic coronaviruses. EDP-235 maintains potency against variants bearing mutations associated with nirmatrelvir resistance. Additionally, EDP-235 demonstrates a ≥ 500-fold selectivity index against multiple host proteases. In a male Syrian hamster model of COVID-19, EDP-235 suppresses SARS-CoV-2 replication and viral-induced hamster lung pathology. In a female ferret model, EDP-235 inhibits production of SARS-CoV-2 infectious virus and RNA at multiple anatomical sites. Furthermore, SARS-CoV-2 contact transmission does not occur when naïve ferrets are co-housed with infected, EDP-235-treated ferrets. Collectively, these results demonstrate that EDP-235 is a broad-spectrum coronavirus inhibitor with efficacy in animal models of primary infection and transmission.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2

Background

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission.

Methods

We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics.

Results

We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein.

Conclusions

This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Study of key residues in MERS-CoV and SARS-CoV-2 main proteases for resistance against clinically applied inhibitors nirmatrelvir and ensitrelvir

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an epidemic, zoonotically emerging pathogen initially reported in Saudi Arabia in 2012. MERS-CoV has the potential to mutate or recombine with other coronaviruses, thus acquiring the ability to efficiently spread among humans and become pandemic. Its high mortality rate of up to 35% and the absence of effective targeted therapies call for the development of antiviral drugs for this pathogen. Since the beginning of the SARS-CoV-2 pandemic, extensive research has focused on identifying protease inhibitors for the treatment of SARS-CoV-2. Our intention was therefore to assess whether these protease inhibitors are viable options for combating MERS-CoV. To that end, we used previously established protease assays to quantify inhibition of SARS-CoV-2, MERS-CoV and other main proteases. Nirmatrelvir inhibited several of these proteases, whereas ensitrelvir was less broadly active. To simulate nirmatrelvir's clinical use against MERS-CoV and subsequent resistance development, we applied a safe, surrogate virus-based system. Using the surrogate virus, we previously selected hallmark mutations of SARS-CoV-2-Mpro, such as T21I, M49L, S144A, E166A/K/V and L167F. In the current study, we selected a pool of MERS-CoV-Mpro mutants, characterized the resistance and modelled the steric effect of catalytic site mutants S142G, S142R, S147Y and A171S.

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Sporadic Occurrence of Ensitrelvir-Resistant SARS-CoV-2, Japan

Using the GISAID EpiCoV database, we identified 256 COVID-19 patients in Japan during March 31-December 31, 2023, who had mutations in the SARS-CoV-2 nonstructural protein 5 conferring ensitrelvir resistance. Ongoing genomic surveillance is required to monitor emergence of SARS-CoV-2 mutations that are resistant to anticoronaviral drugs.

Resistance mechanisms of SARS-CoV-2 3CLpro to the non-covalent inhibitor WU-04

Drug resistance poses a significant challenge in the development of effective therapies against SARS-CoV-2. Here, we identified two double mutations, M49K/M165V and M49K/S301P, in the 3C-like protease (3CLpro) that confer resistance to a novel non-covalent inhibitor, WU-04, which is currently in phase III clinical trials (NCT06197217). Crystallographic analysis indicates that the M49K mutation destabilizes the WU-04-binding pocket, impacting the binding of WU-04 more significantly than the binding of 3CLpro substrates. The M165V mutation directly interferes with WU-04 binding. The S301P mutation, which is far from the WU-04-binding pocket, indirectly affects WU-04 binding by restricting the rotation of 3CLpro's C-terminal tail and impeding 3CLpro dimerization. We further explored 3CLpro mutations that confer resistance to two clinically used inhibitors: ensitrelvir and nirmatrelvir, and revealed a trade-off between the catalytic activity, thermostability, and drug resistance of 3CLpro. We found that mutations at the same residue (M49) can have distinct effects on the 3CLpro inhibitors, highlighting the importance of developing multiple antiviral agents with different skeletons for fighting SARS-CoV-2. These findings enhance our understanding of SARS-CoV-2 resistance mechanisms and inform the development of effective therapeutics.

An orally bioavailable SARS-CoV-2 main protease inhibitor exhibits improved affinity and reduced sensitivity to mutations

Inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function against these mutants are thus urgently needed. We hypothesized that the covalent hepatitis C virus protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more efficiently than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to those of NTV. A crucial feature of ML2006a4 is a derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Last, ML2006a4 was found to be less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory design can preemptively address potential resistance mechanisms to expand future treatment options against coronavirus variants.

Computational Estimation of Residues Involving Resistance to the SARS-CoV-2 Main Protease Inhibitor Ensitrelvir Based on Virtual Alanine Scan of the Active Site

Ensitrelvir is a noncovalent inhibitor of the main protease (Mpro) of severe acute respiratory syndrome coronavirus 2. Acquisition of drug resistance in virus-derived proteins is a serious therapeutic concern, and drug resistance occurs due to amino acid mutations. In this study, we computationally constructed 24 mutants, in which one residue around the active site was replaced with alanine and performed molecular dynamics simulations to the complex of Mpro and ensitrelvir to predict the residues involved in drug resistance. We evaluated the changes in the entire protein structure and ligand configuration in each of these mutants and estimated which residues were involved in ensitrelvir recognition. This method is called a virtual alanine scan. In nine mutants (S1A, T26A, H41A, M49A, L141A, H163A, E166A, V186A, and R188A), although the entire protein structure and catalytic dyad (cysteine (Cys)145 and histidine (His)41) were not significantly moved, the ensitrelvir configuration changed. Thus, it is considered that these mutants did not recognize ensitrelvir while maintaining Mpro enzymatic activities, and Ser1, Thr26, His41, Met49, Leu141, His163, Glu166, Val186, and Arg188 may be related to ensitrelvir resistance. The ligand shift noted in M49A was similar to that observed in M49I, which has been shown to be experimentally ensitrelvir resistant. These findings suggest that our research approach can predict mutations that incite drug resistance.

A comprehensive study of SARS-CoV-2 main protease (Mpro) inhibitor-resistant mutants selected in a VSV-based system

Nirmatrelvir was the first protease inhibitor (PI) specifically developed against the SARS-CoV-2 main protease (3CLpro/Mpro) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate Mpro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-Mpro) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron Mpro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available PIs (nirmatrelvir and ensitrelvir) with cell-based and biochemical assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation Mpro inhibitors, will help to understand SARS-CoV-2 protease-inhibitor-resistance mechanisms and show the relevance of specific mutations in the clinic, thereby informing treatment decisions.

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here, we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.