Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

Exploring covalent inhibitors of SARS-CoV-2 main protease: from peptidomimetics to novel scaffolds

Peptidomimetic inhibitors mimic natural peptide substrates, employing electrophilic warheads to covalently interact with the catalytic Cys145 of Mpro. Examples include aldehydes, α-ketoamides, and aza-peptides, with discussions on their mechanisms of action, potency, and structural insights. Non-peptidomimetic inhibitors utilise diverse scaffolds and mechanisms, achieving covalent modification of Mpro.

AMOEBA Polarizable Molecular Dynamics Simulations of Guanine Quadruplexes: From the c-Kit Proto-Oncogene to HIV-1

Long oligomer sequences, rich in guanine and cytosine, such as c-kit1 and the HIV-1 LTR-III sequence, are prevalent in oncogenes and retroviruses and play crucial roles in cancer. Understanding the conformational dynamics of such guanine quadruplexes and identifying druggable regions are therefore essential for developing new inhibition strategies. In this study, we used extensive AMOEBA polarizable force field molecular dynamics simulations combined with data-driven adaptive sampling and clustering algorithms, reaching a cumulative simulation time of 7.5 μs for c-kit1. Such simulations identified novel structural motives and showcased the flexible loop dynamics, as well as the role of polarizable water in transient stabilization of the G-quadruplex. They also identified two druggable pockets in c-kit1. The 400 ns simulation of the HIV-1 LTR-III sequence confirmed its quadruplex stability and uncovered a potentially druggable cryptic pocket.

Discovery of therapeutic promising natural products to target Kv1.3 channel, a transmembrane protein regulating immune disorders, through multidimensional virtual screening, molecular dynamics simulations and biological validation

Kv1.3 voltage-gated potassium channel, is a transmembrane protein that facilitates K+ movement through cell membranes via its intrinsic pores, regulating the cell signaling cascades, especially in immune disorders. In this paper, we employed multidimensional virtual screening to identify 24 potential Kv1.3 inhibitors from a library of 27,637 compounds, with electrophysiological assays confirming 8 active inhibitors (33.33 % hit rate). Structure-activity relationship (SAR) analysis demonstrated that 4-methylpentyl group in side chain and furan ring in Furanocoumarins skeleton are crucial to the bioactivity of target compounds. Orthogonal projection to latent structures model reveals that increasing the QPlogPo/w of the compound can increase activity. Molecular dynamics simulations revealed key roles of residues (VAL469 and ILE472) as active binding sites of Kv1.3 for binding of specific compound. Notopterol (Z4), the most potent Kv1.3 inhibitor (IC50 = 311.90 ± 1.24 nM), significantly suppressed IFN-γ release from CD4+ T cells, whereas, Kv1.3 inactive compound Z20 at 5 μM showed no significant difference in IFN-γ release from CD4+ T cells. In atopic dermatitis rat model, Notopterol reduced epidermal thickening, IgE, Kv1.3, IL-1β production, and infiltration of CD4+ T cells and mast cells. These findings establish Notopterol as a promising Kv1.3 inhibitor for therapeutic applications in immune disorders.

Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations

Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy is a cornerstone of modern computational drug discovery, which is critical for accelerating therapeutic development. Despite recent progress in molecular dynamics (MD) simulations, such as advanced polarizable force fields and enhanced sampling techniques, estimating absolute binding free energies (ABFEs) remains computationally challenging. To overcome these difficulties, we introduce a highly efficient hybrid methodology that couples the Lambda-adaptive biasing force (Lambda-ABF) scheme with on-the-fly probability enhanced sampling (OPES). This approach achieves up to a 9-fold improvement in sampling efficiency and computational speed compared to the original Lambda-ABF when used in conjunction with the AMOEBA polarizable force field, yielding converged results at a fraction of the cost of standard techniques.

High-Resolution Molecular-Dynamics Simulations of the Pyruvate Kinase Muscle Isoform 1 and 2 (PKM1/2)

Glucose metabolism plays a pivotal role in physiological processes and cancer growth. The final stage of glycolysis, converting phosphoenolpyruvate (PEP) into pyruvate, is catalyzed by the pyruvate kinase (PK) enzyme. Whereas PKM1 is mainly expressed in cells with high energy requirements, PKM2 is preferentially expressed in proliferating cells, including tumor cells. Structural analysis of PKM1 and PKM2 is essential to design new molecules with antitumoral activity. To understand their structural dynamics, we performed extensive high-resolution molecular dynamics (MD) simulations using adaptive sampling techniques coupled to the polarizable AMOEBA force field. Performing more than 6 μs of simulation, we considered all oligomerization states of PKM2 and propose structural insights for PKM1 to further study the PKM2-specific allostery. We focused on key sites including the active site and the natural substrate Fructose Bi-Phosphate (FBP) fixation pocket. Additionally, we present the first MD simulation of biologically active PKM1 and uncover important similarities with its PKM2 counterpart bound to FBP. We also analysed TEPP-46's fixation, a pharmacological activator binding a different pocket, on PKM2 and highlighted the structural differences and similarities compared to PKM2 bound to FBP. Finally, we determined potential new cryptic pockets specific to PKM2 for drug targeting.

Histidine 73 methylation coordinates β-actin plasticity in response to key environmental factors

The functional importance of the methylation of histidine 73 (H73) in actin remains unclear. Focusing on cytoplasmic β-actin, present in all mammalian cells, we use molecular dynamics simulations with a polarizable force field and adaptive sampling to examine the effects of H73 methylation. Our results show that methylation enhances nucleotide binding cleft opening, alters allosteric pathways connecting subdomains 2 and 4 (SD2 and SD4) in G-actin, and affects backdoor openings and inorganic phosphate release in F-actin, as validated by biochemical assays. These effects depend on the nucleotide and ions interacting with the actin. Together, our findings reveal how H73 methylation regulates β-actin plasticity and integrates environmental cues.

Identifying Inhibitor-SARS-CoV2-3CLpro Binding Mechanism Through Molecular Docking, GaMD Simulations, Correlation Network Analysis and MM-GBSA Calculations

The main protease of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known as 3CLpro, is crucial in the virus's life cycle and plays a pivotal role in COVID-19. Understanding how small molecules inhibit 3CLpro's activity is vital for developing anti-COVID-19 therapeutics. To this end, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to enhance the sampling of 3CLpro conformations and conducted correlation network analysis (CNA) to explore the interactions between different structural domains. Our findings indicate that a CNA-identified node in domain II of 3CLpro acts as a conduit, transferring conformational changes from the catalytic regions in domains I and II, triggered by the binding of inhibitors (7YY, 7XB, and Y6G), to domain III, thereby modulating 3CLpro's activity. Normal mode analysis (NMA) and principal component analysis (PCA) revealed that inhibitor binding affects the structural flexibility and collective movements of the catalytic sites and domain III, influencing 3CLpro's function. The binding free energies, predicted by both MM-GBSA and QM/MM-GBSA methods, showed a high correlation with experimental data, validating the reliability of our analyses. Furthermore, residues L27, H41, C44, S46, M49, N142, G143, S144, C145, H163, H164, M165, and E166, identified through residue-based free energy decomposition, present promising targets for the design of anti-COVID-19 drugs and could facilitate the development of clinically effective 3CLpro inhibitors.

Computational exploration of viral cell membrane structures for identifying novel therapeutic target

The membrane proteins of viruses play a critical role, and they shield viruses and takes biochemical mechanisms like sticking to the host cell membrane, merging with them, building new viruses, and breaking free. These steps make sure the virus can infect and multiply. But the membrane proteins of Nipah, Zika, SARS-CoV-2, and Hendra virus can cause special kinds of infections. Nipah and Hendra viruses use their fusion protein to join with the host cell membrane. Their glycoprotein interacts with host receptors. The matrix protein helps to build and support the virus structure. Zika virus relies on its envelope protein to attach and fuse with host cells. Its membrane protein keeps the viral envelope stable. SARS-CoV-2 uses its spike protein to enter host cells and its envelope protein helps assemble new viruses. The membrane protein gives structural stability whereas the nucleocapsid protein interacts with the RNA genome. These viral membranes contain various kinds of lipids and proteins and they make up about 30 % of the membrane area. Yet, scientists find it hard to predict their molecular structure and different biological characters. The coarse-grained molecular dynamics simulations, enhanced sampling methods, and various structural bioinformatics investigations on viral proteins provide reliable scientific data. These investigations reveal viral membrane proteins' structural features, movement patterns, and thermodynamic properties. These computer methods are vital for drug discovery because it allows researchers to find new compounds that target viral membrane proteins to prevent their functions.

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design

Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we have introduced novelty in generative tasks for new molecules such as chemical similarity to a target ligand, molecules grown from known protein bound fragments, and creation of molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, uncommon structures in medicinal chemistry, and poor synthetic accessibility with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. We also allow users to define a set of rules for the structures they would like to exclude during the training process and postfiltering steps. Because our approach relies only on the structure of the target protein, iMiner can be easily adapted for the future development of other inhibitors or small molecule therapeutics of any target protein.

Fluorinated Protein-Ligand Complexes: A Computational Perspective

Fluorine is an element renowned for its unique properties. Its powerful capability to modulate molecular properties makes it an attractive substituent for protein binding ligands; however, the rational design of fluorination can be challenging with effects on interactions and binding energies being difficult to predict. In this Perspective, we highlight how computational methods help us to understand the role of fluorine in protein-ligand binding with a focus on molecular simulation. We underline the importance of an accurate force field, present fluoride channels as a showcase for biomolecular interactions with fluorine, and discuss fluorine specific interactions like the ability to form hydrogen bonds and interactions with aryl groups. We put special emphasis on the disruption of water networks and entropic effects.

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.

Palladium-Catalyzed Synthesis of Linked Bis-Heterocycles─Synthesis and Investigation of Photophysical Properties

A palladium-catalyzed domino C-N coupling/Cacchi reaction is reported. Design of photoluminescent bis-heterocycles, aided by density functional theory calculations, was performed with synthetic yields up to 98%. The photophysical properties of the products accessed via this strategy were part of a comprehensive study that led to broad emission spectra and quantum yields of up to 0.59. Mechanistic experiments confirmed bromoalkynes as competent intermediates, and a density functional theory investigation suggests a pathway involving initial oxidative addition into the cis C-Br bond of the gem-dihaloolefin.

Bench-to-bedside: Innovation of small molecule anti-SARS-CoV-2 drugs in China

The ongoing COVID-19 pandemic has resulted in millions of deaths globally, highlighting the need to develop potent prophylactic and therapeutic strategies against SARS-CoV-2. Small molecule inhibitors (remdesivir, Paxlovid, and molnupiravir) are essential complements to vaccines and play important roles in clinical treatment of SARS-CoV-2. Many advances have been made in development of anti-SARS-CoV-2 inhibitors in China, but progress in discovery and characterization of pharmacological activity, antiviral mechanisms, and clinical efficacy are limited. We review development of small molecule anti-SARS-CoV-2 drugs (azvudine [approved by the NMPA of China on July 25, 2022], VV116 [approved by the NMPA of China on January 29, 2023], FB2001, WPV01, pentarlandir, and cepharanthine) in China and summarize their pharmacological activity, potential mechanisms of action, clinical trials and use, and important milestones in their discovery. The role of structural biology in drug development is also reviewed. Future studies should focus on development of diverse second-generation inhibitors with excellent oral bioavailability, superior plasma half-life, increased antiviral activity against SARS-CoV-2 and its variants, high target specificity, minimal side effects, reduced drug-drug interactions, and improved lung histopathology.

Recent Advances in SARS-CoV-2 Main Protease Inhibitors: From Nirmatrelvir to Future Perspectives

The main protease (Mpro) plays a pivotal role in the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is considered a highly conserved viral target. Disruption of the catalytic activity of Mpro produces a detrimental effect on the course of the infection, making this target one of the most attractive for the treatment of COVID-19. The current success of the SARS-CoV-2 Mpro inhibitor Nirmatrelvir, the first oral drug for the treatment of severe forms of COVID-19, has further focused the attention of researchers on this important viral target, making the search for new Mpro inhibitors a thriving and exciting field for the development of antiviral drugs active against SARS-CoV-2 and related coronaviruses.

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

Discovery of PLpro and Mpro Inhibitors for SARS-CoV-2

There are very few small-molecule antivirals for SARS-CoV-2 that are either currently approved (or emergency authorized) in the US or globally, including remdesivir, molnupiravir, and paxlovid. The increasing number of SARS-CoV-2 variants that have appeared since the outbreak began over three years ago raises the need for continual development of updated vaccines and orally available antivirals in order to fully protect or treat the population. The viral main protease (Mpro) and the papain-like protease (PLpro) are key for viral replication; therefore, they represent valuable targets for antiviral therapy. We herein describe an in vitro screen performed using the 2560 compounds from the Microsource Spectrum library against Mpro and PLpro in an attempt to identify additional small-molecule hits that could be repurposed for SARS-CoV-2. We subsequently identified 2 hits for Mpro and 8 hits for PLpro. One of these hits was the quaternary ammonium compound cetylpyridinium chloride with dual activity (IC50 = 2.72 ± 0.09 μM for PLpro and IC50 = 7.25 ± 0.15 μM for Mpro). A second inhibitor of PLpro was the selective estrogen receptor modulator raloxifene (IC50 = 3.28 ± 0.29 μM for PLpro and IC50 = 42.8 ± 6.7 μM for Mpro). We additionally tested several kinase inhibitors and identified olmutinib (IC50 = 0.54 ± 0.04 μM), bosutinib (IC50 = 4.23 ± 0.28 μM), crizotinib (IC50 = 3.81 ± 0.04 μM), and dacominitinib (IC50 = IC50 3.33 ± 0.06 μM) as PLpro inhibitors for the first time. In some cases, these molecules have also been tested by others for antiviral activity for this virus, or we have used Calu-3 cells infected with SARS-CoV-2. The results suggest that approved drugs can be identified with promising activity against these proteases, and in several cases we or others have validated their antiviral activity. The additional identification of known kinase inhibitors as molecules targeting PLpro may provide new repurposing opportunities or starting points for chemical optimization.

Dynamical Nonequilibrium Molecular Dynamics Simulations Identify Allosteric Sites and Positions Associated with Drug Resistance in the SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro) plays an essential role in the coronavirus lifecycle by catalyzing hydrolysis of the viral polyproteins at specific sites. Mpro is the target of drugs, such as nirmatrelvir, though resistant mutants have emerged that threaten drug efficacy. Despite its importance, questions remain on the mechanism of how Mpro binds its substrates. Here, we apply dynamical nonequilibrium molecular dynamics (D-NEMD) simulations to evaluate structural and dynamical responses of Mpro to the presence and absence of a substrate. The results highlight communication between the Mpro dimer subunits and identify networks, including some far from the active site, that link the active site with a known allosteric inhibition site, or which are associated with nirmatrelvir resistance. They imply that some mutations enable resistance by altering the allosteric behavior of Mpro. More generally, the results show the utility of the D-NEMD technique for identifying functionally relevant allosteric sites and networks including those relevant to resistance.

A Tale of Two Proteases: MPro and TMPRSS2 as Targets for COVID-19 Therapies

Considering the importance of the 2019 outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulting in the coronavirus disease 2019 (COVID-19) pandemic, an overview of two proteases that play an important role in the infection by SARS-CoV-2, the main protease of SARS-CoV-2 (MPro) and the host transmembrane protease serine 2 (TMPRSS2), is presented in this review. After summarising the viral replication cycle to identify the relevance of these proteases, the therapeutic agents already approved are presented. Then, this review discusses some of the most recently reported inhibitors first for the viral MPro and next for the host TMPRSS2 explaining the mechanism of action of each protease. Afterward, some computational approaches to design novel MPro and TMPRSS2 inhibitors are presented, also describing the corresponding crystallographic structures reported so far. Finally, a brief discussion on a few reports found some dual-action inhibitors for both proteases is given. This review provides an overview of two proteases of different origins (viral and human host) that have become important targets for the development of antiviral agents to treat COVID-19.

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy. The DNN/PFF partition can be user-defined allowing for hybrid simulations to include key ingredients of biosimulation such as polarizable solvents, polarizable counter ions, etc.… ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the model's contributions to low-frequency modes of nuclear forces. It primarily evaluates AMOEBA forces while including ANI-2X ones only via correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 μs, we compute charged/uncharged ligand solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are discussed in terms of statistical uncertainty and appear in the range of chemical accuracy compared to experiment. The availability of the Deep-HP computational platform opens the path towards large-scale hybrid DNN simulations, at force-field cost, in biophysics and drug discovery.

The impact of SARS-CoV-2 3CL protease mutations on nirmatrelvir inhibitory efficiency. Computational insights into potential resistance mechanisms

The use of antiviral drugs can promote the appearance of mutations in the target protein that increase the resistance of the virus to the treatment. This is also the case of nirmatrelvir, a covalent inhibitor of the 3CL protease, or main protease, of SARS-CoV-2. In this work we show how the by-residue decomposition of noncovalent interactions established between the drug and the enzyme, in combination with an analysis of naturally occurring mutations, can be used to detect potential mutations in the 3CL protease conferring resistance to nirmatrelvir. We also investigate the consequences of these mutations on the reaction mechanism to form the covalent enzyme-inhibitor complex using QM/MM methods. In particular, we show that the E166V variant of the protease displays smaller binding affinity to nirmatrelvir and larger activation free energy for the formation of the covalent complex, both factors contributing to the observed resistance to the treatment with this drug. The conclusions derived from our work can be used to anticipate the consequences of the introduction of nirmatrelvir in the fitness landscape of the virus and to design new inhibitors adapted to some of the possible resistance mechanisms.

Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy

The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol^-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.

Exploring new catechin derivatives as SARS-CoV-2 Mpro inhibitors from tea by molecular networking, surface plasma resonance, enzyme inhibition, induced fit docking, and metadynamics simulations

SARS-CoV-2 M^pro (Mpro) is the critical cysteine protease in coronavirus viral replication. Tea polyphenols are effective M^pro inhibitors. Therefore, we aim to isolate and synthesize more novel tea polyphenols from Zhenghedabai (ZHDB) white tea methanol-water (MW) extracts that might inhibit COVID-19. Through molecular networking, 33 compounds were identified and divided into 5 clusters. Further, natural products molecular network (MN) analysis showed that MN1 has new phenylpropanoid-substituted ester-catechin (PSEC), and MN5 has the important basic compound type hydroxycinnamoylcatechins (HCCs). Thus, a new PSEC (1, PSEC636) was isolated, which can be further detected in 14 green tea samples. A series of HCCs were synthesized (2-6), including three new acetylated HCCs (3-5). Then we used surface plasmon resonance (SPR) to analyze the equilibrium dissociation constants (K_D) for the interaction of 12 catechins and M^pro. The K_D values of PSEC636 (1), EGC-C (2), and EC-CDA (3) were 2.25, 2.81, and 2.44 μM, respectively. Moreover, compounds 1, 2, and 3 showed the potential M^pro inhibition with IC₅₀ 5.95 ± 0.17, 9.09 ± 0.22, and 23.10 ± 0.69 μM, respectively. Further, we used induced fit docking (IFD), binding pose metadynamics (BPMD), and molecular dynamics (MD) to explore the stable binding pose of M^pro-1, showing that 1 could tightly bond with the amino acid residues THR²⁶, HIS⁴¹, CYS⁴⁴, TYR⁵⁴, GLU¹⁶⁶, and ASP¹⁸⁷. The computer modeling studies reveal that the ester, acetyl, and pyrogallol groups could improve inhibitory activity. Our research suggests that these catechins are effective M^pro inhibitors, and might be developed as therapeutics against COVID-19.

Targeting SARS-CoV-2 Main Protease for Treatment of COVID-19: Covalent Inhibitors Structure-Activity Relationship Insights and Evolution Perspectives

The viral main protease is one of the most attractive targets among all key enzymes involved in the SARS-CoV-2 life cycle. Covalent inhibition of the cysteine145 of SARS-CoV-2 MPRO with selective antiviral drugs will arrest the replication process of the virus without affecting human catalytic pathways. In this Perspective, we analyzed the in silico, in vitro, and in vivo data of the most representative examples of covalent SARS-CoV-2 MPRO inhibitors reported in the literature to date. In particular, the studied molecules were classified into eight different categories according to their reactive electrophilic warheads, highlighting the differences between their reversible/irreversible mechanism of inhibition. Furthermore, the analyses of the most recurrent pharmacophoric moieties and stereochemistry of chiral carbons were reported. The analyses of noncovalent and covalent in silico protocols, provided in this Perspective, would be useful for the scientific community to discover new and more efficient covalent SARS-CoV-2 MPRO inhibitors.

The SARS-CoV-2 main protease (Mpro): Structure, function, and emerging therapies for COVID-19

The main proteases (Mpro), also termed 3-chymotrypsin-like proteases (3CLpro), are a class of highly conserved cysteine hydrolases in β-coronaviruses. Increasing evidence has demonstrated that 3CLpros play an indispensable role in viral replication and have been recognized as key targets for preventing and treating coronavirus-caused infectious diseases, including COVID-19. This review is focused on the structural features and biological function of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease Mpro (also known as 3CLpro), as well as recent advances in discovering and developing SARS-CoV-2 3CLpro inhibitors. To better understand the characteristics of SARS-CoV-2 3CLpro inhibitors, the inhibition activities, inhibitory mechanisms, and key structural features of various 3CLpro inhibitors (including marketed drugs, peptidomimetic, and non-peptidomimetic synthetic compounds, as well as natural compounds and their derivatives) are summarized comprehensively. Meanwhile, the challenges in this field are highlighted, while future directions for designing and developing efficacious 3CLpro inhibitors as novel anti-coronavirus therapies are also proposed. Collectively, all information and knowledge presented here are very helpful for understanding the structural features and inhibitory mechanisms of SARS-CoV-2 3CLpro inhibitors, which offers new insights or inspiration to medicinal chemists for designing and developing more efficacious 3CLpro inhibitors as novel anti-coronavirus agents.

Singular Interface Dynamics of the SARS-CoV-2 Delta Variant Explained with Contact Perturbation Analysis

Emerging SARS-CoV-2 variants raise concerns about our ability to withstand the Covid-19 pandemic, and therefore, understanding mechanistic differences of those variants is crucial. In this study, we investigate disparities between the SARS-CoV-2 wild type and five variants that emerged in late 2020, focusing on the structure and dynamics of the spike protein interface with the human angiotensin-converting enzyme 2 (ACE2) receptor, by using crystallographic structures and extended analysis of microsecond molecular dynamics simulations. Dihedral angle principal component analysis (PCA) showed the strong similarities in the spike receptor binding domain (RBD) dynamics of the Alpha, Beta, Gamma, and Delta variants, in contrast with those of WT and Epsilon. Dynamical perturbation networks and contact PCA identified the peculiar interface dynamics of the Delta variant, which cannot be directly imputable to its specific L452R and T478K mutations since those residues are not in direct contact with the human ACE2 receptor. Our outcome shows that in the Delta variant the L452R and T478K mutations act synergistically on neighboring residues to provoke drastic changes in the spike/ACE2 interface; thus a singular mechanism of action eventually explains why it dominated over preceding variants.