Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease

Improved expression and purification of highly-active 3 chymotrypsin-like protease from SARS-CoV-2

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the causative pathogen of coronavirus disease-19 (COVID-19). The COVID-19 pandemic has resulted in millions of deaths and widespread socio-economic damage worldwide. Therefore, numerous studies have been conducted to identify effective measures to control the spreading of the virus. Among various potential targets, the 3 chymotrypsin-like protease (3CLpro), also known as Mpro, stands out as the key protease of SARS-CoV-2, playing an essential role in virus replication and assembly, is the most prospective. In this study, we modified the commercial vector, pETM33-Nsp5-Mpro (plasmid # 156475, Addgene, USA), by inserting an autocleavage site (AVLQ) of 3CLpro and 6 × His-tag encoding sequences before and after the Nsp5-Mpro sequence, respectively. This modification enabled the expression of 3CLpro as an authentic N terminal protease (au3CLpro), which was purified to electrophoretic homogeneity by a single-step chromatography using two tandem Glutathione- and Ni-Sepharose columns. The enzyme au3CLpro demonstrated significantly higher activity (3169 RFU/min/μg protein) and catalytic efficiency (Kcat/Km of 0.007 μM-1.s-1) than that of the 3CLpro (com3CLpro) expressed from the commercial vector (pETM33-Nsp5-Mpro) with specific activity 889 RFU/min/μg and Kcat/Km of 0.0015 μM-1.s-1, respectively. Optimal conditions for au3CLpro activity included a 50 mM Tris-HCl buffer at pH 7, containing 150 mM NaCl and 0.1 mg/ml BSA at 37 °C.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods

The main protease of SARS-CoV-2 (called M^pro or 3CL^pro) is essential for processing polyproteins encoded by viral RNA. Several M^pro mutations were found in SARS-CoV-2 variants, which are related to higher transmissibility, pathogenicity, and resistance to neutralization antibodies. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. In this study, we used a hybrid simulation method to generate intermediate structures along the six lowest frequency normal modes and sample the conformational space and characterize the structural dynamics and global motions of WT SARS-CoV-2 M^pro and 48 mutations, including mutations found in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. We tried to contribute to the elucidation of the effects of mutation in the structural dynamics of SARS-CoV-2 M^pro. A machine learning analysis was performed following the investigation regarding the influence of the K90R, P99L, P108S, and N151D mutations on the dimeric interface assembling of the SARS-CoV-2 M^pro. The parameters allowed the selection of potential structurally stable dimers, which demonstrated that some single surface aa substitutions not located at the dimeric interface (K90R, P99L, P108S, and N151D) are able to induce significant quaternary changes. Furthermore, our results demonstrated, by a Quantum Mechanics method, the influence of SARS-CoV-2 M^pro mutations on the catalytic mechanism, confirming that only one of the chains of the WT and mutant SARS-CoV-2 M^pros are prone to cleave substrates. Finally, it was also possible to identify the aa residue F140 as an important factor related to the increasing enzymatic reactivity of a significant number of SARS-CoV-2 M^pro conformations generated by the normal modes-based simulations.

Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease

The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.

Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin

Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, this framework is applied to aggregates of γD-crystallin, a structural protein of the eye lens implicated in cataract disease. The NHMs in this study are generated from atomistic simulations of equilibrium distributions of wild-type and the cataract-causing variant W42R in solution, performed by Wong, E. K.; Prytkova, V.; Freites, J. A.; Butts, C. T.; Tobias, D. J. Molecular Mechanism of Aggregation of the Cataract-Related γD-Crystallin W42R Variant from Multiscale Atomistic Simulations. Biochemistry2019, 58 (35), 3691-3699. Network models are shown to successfully reproduce the aggregate size and structure observed in the atomistic simulation, and provide information about the transient protein-protein interactions therein. The system size is scaled from the original 375 monomers to a system of 10000 monomers, revealing a lowering of the upper tail of the aggregate size distribution of the W42R variant. Extrapolation to higher and lower concentrations is also performed. These results provide an example of the utility of NHMs for coarse-grained simulation of protein systems, as well as their ability to scale to large system sizes and high concentrations, reducing computational costs while retaining topological information about the system.

A strategy for evaluating potential antiviral resistance to small molecule drugs and application to SARS-CoV-2

Alterations in viral fitness cannot be inferred from only mutagenesis studies of an isolated viral protein. To-date, no systematic analysis has been performed to identify mutations that improve virus fitness and reduce drug efficacy. We present a generic strategy to evaluate which viral mutations might diminish drug efficacy and applied it to assess how SARS-CoV-2 evolution may affect the efficacy of current approved/candidate small-molecule antivirals for Mpro, PLpro, and RdRp. For each drug target, we determined the drug-interacting virus residues from available structures and the selection pressure of the virus residues from the SARS-CoV-2 genomes. This enabled the identification of promising drug target regions and small-molecule antivirals that the virus can develop resistance. Our strategy of utilizing sequence and structural information from genomic sequence and protein structure databanks can rapidly assess the fitness of any emerging virus variants and can aid antiviral drug design for future pathogens.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

Structural Basis for the Inhibition of Coronaviral Main Proteases by a Benzothiazole-Based Inhibitor

The ongoing spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused hundreds of millions of cases and millions of victims worldwide with serious consequences to global health and economies. Although many vaccines protecting against SARS-CoV-2 are currently available, constantly emerging new variants necessitate the development of alternative strategies for prevention and treatment of COVID-19. Inhibitors that target the main protease (M^pro) of SARS-CoV-2, an essential enzyme that promotes viral maturation, represent a key class of antivirals. Here, we showed that a peptidomimetic compound with benzothiazolyl ketone as warhead, YH-53, is an effective inhibitor of SARS-CoV-2, SARS-CoV, and MERS-CoV M^pros. Crystal structures of M^pros from SARS-CoV-2, SARS-CoV, and MERS-CoV bound to the inhibitor YH-53 revealed a unique ligand-binding site, which provides new insights into the mechanism of inhibition of viral replication. A detailed analysis of these crystal structures defined the key molecular determinants required for inhibition and illustrate the binding mode of M^pros from other coronaviruses. In consideration of the important role of M^pro in developing antivirals against coronaviruses, insights derived from this study should add to the design of pan-coronaviral M^pro inhibitors that are safer and more effective.

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices

The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety of approximations or other workarounds. Markov Chain Monte Carlo maximum likelihood (MCMC MLE) provides a powerful tool to approximate the maximum likelihood estimator (MLE) of ERGM parameters, and is generally feasible for typical models on single networks with as many as a few thousand nodes. MCMC-based algorithms for Bayesian analysis are more expensive, and high-quality answers are challenging to obtain on large graphs. For both strategies, extension to the pooled case—in which we observe multiple networks from a common generative process—adds further computational cost, with both time and memory scaling linearly in the number of graphs. This becomes prohibitive for large networks, or cases in which large numbers of graph observations are available. Here, we exploit some basic properties of the discrete exponential families to develop an approach for ERGM inference in the pooled case that (where applicable) allows an arbitrarily large number of graph observations to be fit at no additional computational cost beyond preprocessing the data itself. Moreover, a variant of our approach can also be used to perform Bayesian inference under conjugate priors, again with no additional computational cost in the estimation phase. The latter can be employed either for single graph observations, or for observations from graph sets. As we show, the conjugate prior is easily specified, and is well-suited to applications such as regularization. Simulation studies show that the pooled method leads to estimates with good frequentist properties, and posterior estimates under the conjugate prior are well-behaved. We demonstrate the usefulness of our approach with applications to pooled analysis of brain functional connectivity networks and to replicated x-ray crystal structures of hen egg-white lysozyme.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CL^pro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CL^pro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CL^pro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Potential Resistance of SARS-CoV-2 Main Protease (Mpro) against Protease Inhibitors: Lessons Learned from HIV-1 Protease

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome 2 (SARS-CoV-2), has been one of the most devastating pandemics of recent times. The lack of potent novel antivirals had led to global health crises; however, emergence and approval of potent inhibitors of the viral main protease (Mpro), such as Pfizer's newly approved nirmatrelvir, offers hope not only in the therapeutic front but also in the context of prophylaxis against the infection. By their nature, RNA viruses including human immunodeficiency virus (HIV) have inherently high mutation rates, and lessons learnt from previous and currently ongoing pandemics have taught us that these viruses can easily escape selection pressure through mutation of vital target amino acid residues in monotherapeutic settings. In this paper, we review nirmatrelvir and its binding to SARS-CoV-2 Mpro and draw a comparison to inhibitors of HIV protease that were rendered obsolete by emergence of resistance mutations, emphasizing potential pitfalls in the design of inhibitors that may be of important relevance to the long-term use of novel inhibitors against SARS-CoV-2.

Inhibition of the main protease of SARS-CoV-2 (Mpro) by repurposing/designing drug-like substances and utilizing nature's toolbox of bioactive compounds

The emergence of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has resulted in a long pandemic, with numerous cases and victims worldwide and enormous consequences on social and economic life. Although vaccinations have proceeded and provide a valuable shield against the virus, the approved drugs are limited and it is crucial that further ways to combat infection are developed, that can also act against potential mutations. The main protease (Mpro) of the virus is an appealing target for the development of inhibitors, due to its importance in the viral life cycle and its high conservation among different coronaviruses. Several compounds have shown inhibitory potential against Mpro, both in silico and in vitro, with few of them also having entered clinical trials. These candidates include: known drugs that have been repurposed, molecules specifically designed based on the natural substrate of the protease or on structural moieties that have shown high binding affinity to the protease active site, as well as naturally derived compounds, either isolated or in plant extracts. The aim of this work is to collectively present the results of research regarding Mpro inhibitors to date, focusing on the function of the compounds founded by in silico simulations and further explored by in vitro and in vivo assays. Creating an extended portfolio of promising compounds that may block viral replication by inhibiting Mpro and by understanding involved structure-activity relationships, could provide a basis for the development of effective solutions against SARS-CoV-2 and future related outbreaks.

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors

A series of SARS-CoV-2 main protease (SARS-CoV-2-M^pro) inhibitors were modeled using evolutive grammar algorithms. We have generated an automated program that finds the best candidate to inhibit the main protease, M^pro, of SARS-CoV-2. The candidates were constructed based on a pharmacophore model of the above-mentioned target; relevant moieties of such molecules were modified using data-basis sets with similar chemical behavior to the reference moieties. Additionally, we used the SMILES language to translate 3D chemical structures to 1D words; then, an evolutive grammar algorithm was used to explore the chemical space and obtain new candidates, which were evaluated via the binding energy of molecular coupling assays as an evaluation function. Finally, sixteen molecules were obtained in 3 runs of our program, three of which show promising binding properties as SARS-CoV-2-M^pro inhibitors. One of them, TTO, maintained its relevant binding properties during 100 ns molecular dynamics experiments. For this reason, TTO is the best candidate to inhibit SARS-CoV-2-M^pro. The software we developed for this contribution is available at the following URL: https://github.com/masotelof/GEMolecularDesign.

Thermodynamic and structural insights into the repurposing of drugs that bind to SARS-CoV-2 main protease

Although researchers have been working tirelessly since the COVID-19 outbreak, so far only three drugs - remdesivir, ronapreve and molnupiravir - have been approved for use in some countries which directly target the SARS-CoV-2 virus. Given the slow pace and substantial costs of new drug discovery and development, together with the urgency of the matter, repurposing of existing drugs for the ongoing disease is an attractive proposition. In a recent study, a high-throughput X-ray crystallographic screen was performed for a selection of drugs which have been approved or are in clinical trials. Thirty-seven compounds have been identified from drug libraries all of which bind to the SARS-CoV-2 main protease (3CL^pro). In the current study, we use molecular dynamics simulation and an ensemble-based free energy approach, namely, enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS), to investigate a subset of the aforementioned compounds. The drugs studied here are highly diverse, interacting with different binding sites and/or subsites of 3CL^pro. The predicted free energies are compared with experimental results wherever they are available and they are found to be in excellent agreement. Our study also provides detailed energetic insights into the nature of the associated drug-protein binding, in turn shedding light on the design and discovery of potential drugs.

Targeting SARS-CoV-2 Proteases for COVID-19 Antiviral Development

The emergence of severe acute respiratory syndrome (SARS-CoV-2) in 2019 marked the third occurrence of a highly pathogenic coronavirus in the human population since 2003. As the death toll surpasses 5 million globally and economic losses continue, designing drugs that could curtail infection and disease progression is critical. In the US, three highly effective Food and Drug Administration (FDA)-authorized vaccines are currently available, and Remdesivir is approved for the treatment of hospitalized patients. However, moderate vaccination rates and the sustained evolution of new viral variants necessitate the ongoing search for new antivirals. Several viral proteins have been prioritized as SARS-CoV-2 antiviral drug targets, among them the papain-like protease (PLpro) and the main protease (Mpro). Inhibition of these proteases would target viral replication, viral maturation, and suppression of host innate immune responses. Knowledge of inhibitors and assays for viruses were quickly adopted for SARS-CoV-2 protease research. Potential candidates have been identified to show inhibitory effects against PLpro and Mpro, both in biochemical assays and viral replication in cells. These results encourage further optimizations to improve prophylactic and therapeutic efficacy. In this review, we examine the latest developments of potential small-molecule inhibitors and peptide inhibitors for PLpro and Mpro, and how structural biology greatly facilitates this process.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

Design of modular autoproteolytic gene switches responsive to anti-coronavirus drug candidates

The main (Mpro) and papain-like (PLpro) proteases encoded by SARS-CoV-2 are essential to process viral polyproteins into functional units, thus representing key targets for anti-viral drug development. There is a need for an efficient inhibitor screening system that can identify drug candidates in a cellular context. Here we describe modular, tunable autoproteolytic gene switches (TAGS) relying on synthetic transcription factors that self-inactivate, unless in the presence of coronavirus protease inhibitors, consequently activating transgene expression. TAGS rapidly report the impact of drug candidates on Mpro and PLpro activities with a high signal-to-noise response and a sensitivity matching concentration ranges inhibiting viral replication. The modularity of the TAGS enabled the study of other Coronaviridae proteases, characterization of mutations and multiplexing of gene switches in human cells. Mice implanted with Mpro or PLpro TAGS-engineered cells enabled analysis of the activity and bioavailability of protease inhibitors in vivo in a virus-free setting.

Peptidomimetic nitrile warheads as SARS-CoV-2 3CL protease inhibitors

Tragically, the death toll from the COVID-19 pandemic continues to rise, and with variants being observed around the globe new therapeutics, particularly direct-acting antivirals that are easily administered, are desperately needed. Studies targeting the SARS-CoV-2 3CL protease, which is critical for viral replication, with different peptidomimetics and warheads is an active area of research for development of potential drugs. To date, however, only a few publications have evaluated the nitrile warhead as a viral 3CL protease inhibitor, with only modest activity reported. This article describes our investigation of P3 4-methoxyindole peptidomimetic analogs with select P1 and P2 groups with a nitrile warhead that are potent inhibitors of SARS-CoV-2 3CL protease and demonstrate in vitro SARS-CoV-2 antiviral activity. A selectivity for SARS-CoV-2 3CL protease over human cathepsins B, S and L was also observed with the nitrile warhead, which was superior to that with the aldehyde warhead. A co-crystal structure with SARS-CoV-2 3CL protease and a reversibility study indicate that a reversible, thioimidate adduct is formed when the catalytic sulfur forms a covalent bond with the carbon of the nitrile. This effort also identified efflux as a property limiting antiviral activity of these compounds, and together with the positive attributes described these results provide insight for further drug development of novel nitrile peptidomimetics targeting SARS-CoV-2 3CL protease.

A cyclic peptide inhibitor of the SARS-CoV-2 main protease

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (M^pro). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of M^pro. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of M^pro. In vitro evaluation of the cyclic peptide inhibitor reveals that the inhibitor exhibits modest activity against M^pro and does not appear to be cleaved by the enzyme. Conformational searching predicts that the cyclic peptide inhibitor is fairly rigid, adopting a favorable conformation for binding to the active site of M^pro. Computational docking to the SARS-CoV-2 M^pro suggests that the cyclic peptide inhibitor can bind the active site of M^pro in the predicted manner. Molecular dynamics simulations provide further insights into how the cyclic peptide inhibitor may bind the active site of M^pro. Although the activity of the cyclic peptide inhibitor is modest, its design and study lays the groundwork for the development of additional cyclic peptide inhibitors against M^pro with improved activities.

In Silico Characterization of Masitinib Interaction with SARS-CoV-2 Main Protease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to be a global health problem. Despite the current implementation of COVID-19 vaccination schedules, identifying effective antiviral drug treatments for this disease continues to be a priority. A recent study showed that masitinib (MST), a tyrosine kinase inhibitor, blocks the proteolytic activity of SARS-CoV-2 main protease (Mpro ). Although MST is a potential candidate for COVID-19 treatment, a comprehensive analysis of its interaction with Mpro has not been done. In this work, we performed molecular dynamics simulations of the MST-Mpro complex crystal structure. The effect of the protonation states of Mpro H163 residue and MST titratable groups were studied. Furthermore, we identified the MST substituents and Mpro mutations that affect the stability of the complex. Our results provide valuable insights into the design of new MST analogs as potential treatments for COVID-19.

Large-Scale Recombinant Production of the SARS-CoV-2 Proteome for High-Throughput and Structural Biology Applications

The highly infectious disease COVID-19 caused by the Betacoronavirus SARS-CoV-2 poses a severe threat to humanity and demands the redirection of scientific efforts and criteria to organized research projects. The international COVID19-NMR consortium seeks to provide such new approaches by gathering scientific expertise worldwide. In particular, making available viral proteins and RNAs will pave the way to understanding the SARS-CoV-2 molecular components in detail. The research in COVID19-NMR and the resources provided through the consortium are fully disclosed to accelerate access and exploitation. NMR investigations of the viral molecular components are designated to provide the essential basis for further work, including macromolecular interaction studies and high-throughput drug screening. Here, we present the extensive catalog of a holistic SARS-CoV-2 protein preparation approach based on the consortium's collective efforts. We provide protocols for the large-scale production of more than 80% of all SARS-CoV-2 proteins or essential parts of them. Several of the proteins were produced in more than one laboratory, demonstrating the high interoperability between NMR groups worldwide. For the majority of proteins, we can produce isotope-labeled samples of HSQC-grade. Together with several NMR chemical shift assignments made publicly available on covid19-nmr.com, we here provide highly valuable resources for the production of SARS-CoV-2 proteins in isotope-labeled form.

Screening of potent phytochemical inhibitors against SARS-CoV-2 protease and its two Asian mutants

Background

COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide.

Object

The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19.

Methods

Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (M^pro) of SARS-CoV-2 (PDB ID: 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time.

Results

In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their docking scores. Among the 2,360 compounds, 12 lead compounds were selected based on their docking score. Kazinol T with NPASS ID: NPC474104 showed the highest docking score of −14.355 and passed criteria of Lipinski's drug-like parameters. Monitoring ADMET properties, Kazinol T showed its safety for consumption. Docking of Kazinol T with two Asian mutants (R60C and I152V) showed variations in binding and energy parameters. Normal mode analysis for ligand-bound and unbound form of protease along with its mutants, revealed displacement and correlation parameters for C-alpha atoms. MD simulation results showed that all ligand-protein complexes remained intact and stable in a dynamic environment with negative Gibbs free energy.

Conclusions

The natural product Kazinol T was a predicted lead compound against the main protease of SARS-CoV-2 and will be the possible treatment for COVID-19.

Identification of mutation resistance coldspots for targeting the SARS-CoV2 main protease

Mutations in the novel coronavirus SARS-CoV2 are the major concern as they might lead to drug/vaccine resistance. In the host cell, the virus largely depends on the main protease (M^pro ) to regulate infection hence it is one of the most attractive targets for inhibitor design. However, >19,000 mutations in the M^pro have already been reported. The mutations encompassing 282 amino acid positions and these "hotspots" might change the M^pro structure, activity and potentially delay therapeutic strategies targeting M^pro . Thus, here we identified 24 mutational "coldspots" where mutations have not been observed. We compared the structure-function relationship of these coldspots with several SARS-CoV2 M^pro X-ray crystal structures. We found that three coldspot residues (Leu141, Phe185, and Gln192) help to form the active site, while seven (Gly2, Arg4, Tyr126, Lys137, Leu141, Leu286, and Leu287) contribute to dimer formation that is required for M^pro activity. The surface of the dimer interface is more resistant to mutations compared to the active site. Interestingly, most of the coldspots are found in three clusters and forms conserved patterns when compared with other coronaviruses. Importantly, several conserved coldspots are available on the surface of the active site and at the dimer interface for targeting. The identification and short list of these coldspots offers a new perspective to target the SARS-CoV2 M^pro while avoiding mutation-based drug resistance.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.