New insights into the catalytic mechanism of the SARS-CoV-2 main protease: an ONIOM QM/MM approach

Covalent small-molecule inhibitors of SARS-CoV-2 Mpro: Insights into their design, classification, biological activity, and binding interactions

Since 2020, many compounds have been investigated for their potential use in the treatment of SARS-CoV-2 infection. Among these agents, a huge number of natural products and FDA-approved drugs have been evaluated as potential therapeutics for SARS-CoV-2 using virtual screening and docking studies. However, the identification of the molecular targets involved in viral replication led to the development of rationally designed anti-SARS-CoV-2 agents. Among these targets, the main protease (Mpro) is one of the key enzymes needed in the replication of the virus. The data gleaned from the crystal structures of SARS-CoV-2 Mpro complexes with small-molecule covalent inhibitors has been used in the design and discovery of many highly potent and broad-spectrum Mpro inhibitors. The current review focuses mainly on the covalent type of SARS-CoV-2 Mpro inhibitors. The design, chemistry, and classification of these inhibitors were also in focus. The biological activity of these inhibitors, including their inhibitory activities against Mpro, their antiviral activities, and the SAR studies, were discussed. The review also describes the potential mechanism of the interaction between these inhibitors and the catalytic Cys145 residue in Mpro. Moreover, the binding modes and key binding interactions of these covalent inhibitors were also illustrated. The covalent inhibitors discussed in this review were of diverse chemical nature and origin. Their antiviral activity was mediated mainly by the inhibition of SARS-CoV-2 Mpro, with IC50 values in the micromolar to the nanomolar range. Many of these inhibitors exhibited broad-spectrum inhibitory activity against the Mpro enzymes of other coronaviruses (SARS-CoV-1 and MERS-CoV). The dual inhibition of the Mpro and PLpro enzymes of SARS-CoV-2 could also provide higher therapeutic benefits than Mpro inhibition. Despite the approval of nirmatrelvir by the FDA, many mutations in the Mpro enzyme of SARS-CoV-2 have been reported. Although some of these mutations did not affect the potency of nirmatrelvir, there is an urgent need to develop a second generation of Mpro inhibitors. We hope that the data summarized in this review could help researchers in the design of a new potent generation of SARS-CoV-2 Mpro inhibitors.

Advances in the Search for SARS-CoV-2 Mpro and PLpro Inhibitors

SARS-CoV-2 is a spherical, positive-sense, single-stranded RNA virus with a large genome, responsible for encoding both structural proteins, vital for the viral particle's architecture, and non-structural proteins, critical for the virus's replication cycle. Among the non-structural proteins, two cysteine proteases emerge as promising molecular targets for the design of new antiviral compounds. The main protease (Mpro) is a homodimeric enzyme that plays a pivotal role in the formation of the viral replication-transcription complex, associated with the papain-like protease (PLpro), a cysteine protease that modulates host immune signaling by reversing post-translational modifications of ubiquitin and interferon-stimulated gene 15 (ISG15) in host cells. Due to the importance of these molecular targets for the design and development of novel anti-SARS-CoV-2 drugs, the purpose of this review is to address aspects related to the structure, mechanism of action and strategies for the design of inhibitors capable of targeting the Mpro and PLpro. Examples of covalent and non-covalent inhibitors that are currently being evaluated in preclinical and clinical studies or already approved for therapy will be also discussed to show the advances in medicinal chemistry in the search for new molecules to treat COVID-19.

State-of-the-art local correlation methods enable affordable gold standard quantum chemistry for up to hundreds of atoms

In this feature, we review the current capabilities of local electron correlation methods up to the coupled cluster model with single, double, and perturbative triple excitations [CCSD(T)], which is a gold standard in quantum chemistry. The main computational aspects of the local method types are assessed from the perspective of applications, but the focus is kept on how to achieve chemical accuracy (i.e., <1 kcal mol-1 uncertainty), as well as on the broad scope of chemical problems made accessible. The performance of state-of-the-art methods is also compared, including the most employed DLPNO and, in particular, our local natural orbital (LNO) CCSD(T) approach. The high accuracy and efficiency of the LNO method makes chemically accurate CCSD(T) computations accessible for molecules of hundreds of atoms with resources affordable to a broad computational community (days on a single CPU and 10-100 GB of memory). Recent developments in LNO-CCSD(T) enable systematic convergence and robust error estimates even for systems of complicated electronic structure or larger size (up to 1000 atoms). The predictive power of current local CCSD(T) methods, usually at about 1-2 order of magnitude higher cost than hybrid density functional theory (DFT), has become outstanding on the palette of computational chemistry applicable for molecules of practical interest. We also review more than 50 LNO-based and other advanced local-CCSD(T) applications for realistic, large systems across molecular interactions as well as main group, transition metal, bio-, and surface chemistry. The examples show that properly executed local-CCSD(T) can contribute to binding, reaction equilibrium, rate constants, etc. which are able to match measurements within the error estimates. These applications demonstrate that modern, open-access, and broadly affordable local methods, such as LNO-CCSD(T), already enable predictive computations and atomistic insight for complicated, real-life molecular processes in realistic environments.

The β-Ketoacyl-ACP Synthase FabF Catalyzes Carbon-Carbon Bond Formation in a Bimodal Pattern for Fatty Acid Biosynthesis

Fatty acids produced by the type-II fatty acid biosynthetic pathway (FAS-II) are essential biomaterials for bacterial membrane construction and numerous metabolic routes. The β-ketoacyl-ACP synthase FabF catalyzes the key C-C bond formation step for fatty acid elongation in FAS-II. Here, we revealed the substrate recognition and catalytic mechanisms of FabF by determining FabF-ACP complexes. FabF displays a distinctive bimodal catalytic pattern specifically on C6 and C10 acyl-ACP substrates. It utilizes positively charged residues located on the η3-helix and loop1 regions near the catalytic tunnel entrance to bind ACP, and two hydrophobic cavities as well as "front", "middle", and "back" door residues to specifically stabilize C6 and C10 acyl substrates for preferential catalysis. Further quantum chemistry calculations suggest that the FabF catalytic residues Lys336 and His304 facilitate proton transfer during condensation catalysis and C-C bond formation. Our results provide key mechanistic insights into the biosynthesis of molecular carbon skeletons based on ketosynthases that are highly conserved through the FAS and polyketide synthase (PKS) analogous biosynthetic routes, broaden the understanding of the tricarboxylic acid cycle that utilizes lipoic acid derived from C8-ACP accumulated due to the FabF distinctive catalytic pattern for oxidative decarboxylations, and may facilitate the development of narrow-spectrum antibacterial drugs.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Elucidation of the catalytic mechanism of a single-metal dependent homing endonuclease using QM and QM/MM approaches: the case study of I-PpoI

Homing endonucleases (HEs) are highly specific DNA cleaving enzymes, with I-PpoI having been suggested to use a single metal to accelerate phosphodiester bond cleavage. Although an I-PpoI mechanism has been proposed based on experimental structural data, no consensus has been reached regarding the roles of the metal or key active site amino acids. This study uses QM cluster and QM/MM calculations to provide atomic-level details of the I-PpoI catalytic mechanism. Minimal QM cluster and large-scale QM/MM models demonstrate that the experimentally-proposed pathway involving direct Mg2+ coordination to the substrate coupled with leaving group protonation through a metal-activated water is not feasible due to an inconducive I-PpoI active site alignment. Despite QM cluster models of varying size uncovering a pathway involving leaving group protonation by a metal-activated water, indirect (water-mediated) metal coordination to the substrate is required to afford this pathway, which renders this mechanism energetically infeasible. Instead, QM cluster models reveal that the preferred pathway involves direct Mg2+-O3' coordination to stabilize the charged substrate and assist leaving group departure, while H98 activates the water nucleophile. These calculations also underscore that both catalytic residues that directly interact with the substrate and secondary amino acids that position or stabilize these residues are required for efficient catalysis. QM/MM calculations on the solvated enzyme-DNA complex verify the preferred mechanism, which is fully consistent with experimental kinetic, structural, and mutational data. The fundamental understanding of the I-PpoI mechanism of action, gained from the present work can be used to further explore potential uses of this enzyme in biotechnology and medicine, and direct future computational investigations of other members of the understudied HE family.

Computational study on the mechanisms of inhibition of SARS-CoV-2 Mpro by aldehyde warheads based on DFT

SARS-CoV-2 main protease, Mpro, plays a crucial role in the virus replication cycle, making it an important target for antiviral research. In this study, a simplified model obtained through truncation is used to explore the reaction mechanism of aldehyde warhead compounds inhibiting Mpro at the level of density functional theory. According to the calculation results, proton transfer (P_T)-nucleophilic attack (N_A) is the rate-determining step in the entire reaction pathway. The water molecule that plays a catalytic role occupies the oxyanion hole, which is unfavorable for the aldehyde warhead to approach the Cys145 SH. Through a hypothetical study of substituting the main chain NH with methylene, it is further confirmed that the P_T-N_A is a proton transfer-dominated process accompanied by a nucleophilic attack reaction. In this process, the oxyanion hole serves only to stabilize the aldehyde oxygen anion and therefore does not have a significant impact on the activation free energy barrier of the step. Our research results provide a unique perspective for understanding the covalent inhibition reaction of the Mpro active site. This study also offers theoretical guidance for the design of new Mpro covalent inhibitors.

Multiscale Simulations of the Covalent Inhibition of the SARS-CoV-2 Main Protease: Four Compounds and Three Reaction Mechanisms

We report the results of computational modeling of the reactions of the SARS-CoV-2 main protease (MPro) with four potential covalent inhibitors. Two of them, carmofur and nirmatrelvir, have shown experimentally the ability to inhibit MPro. Two other compounds, X77A and X77C, were designed computationally in this work. They were derived from the structure of X77, a non-covalent inhibitor forming a tight surface complex with MPro. We modified the X77 structure by introducing warheads capable of reacting with the catalytic cysteine residue in the MPro active site. The reaction mechanisms of the four molecules with MPro were investigated by quantum mechanics/molecular mechanics (QM/MM) simulations. The results show that all four compounds form covalent adducts with the catalytic cysteine Cys 145 of MPro. From the chemical perspective, the reactions of these four molecules with MPro follow three distinct mechanisms. The reactions are initiated by a nucleophilic attack of the thiolate group of the deprotonated cysteine residue from the catalytic dyad Cys145-His41 of MPro. In the case of carmofur and X77A, the covalent binding of the thiolate to the ligand is accompanied by the formation of the fluoro-uracil leaving group. The reaction with X77C follows the nucleophilic aromatic substitution SNAr mechanism. The reaction of MPro with nirmatrelvir (which has a reactive nitrile group) leads to the formation of a covalent thioimidate adduct with the thiolate of the Cys145 residue in the enzyme active site. Our results contribute to the ongoing search for efficient inhibitors of the SARS-CoV-2 enzymes.

Unraveling the catalytic mechanism of SARS-CoV-2 papain-like protease with allosteric modulation of C270 mutation using multiscale computational approaches

Papain-like protease (PL^pro) is a promising therapeutic target against SARS-CoV-2, but its restricted S1/S2 subsites pose an obstacle in developing active site-directed inhibitors. We have recently identified C270 as a novel covalent allosteric site for SARS-CoV-2 PL^pro inhibitors. Here we present a theoretical investigation of the proteolysis reaction catalyzed by the wild-type SARS-CoV-2 PL^pro as well as the C270R mutant. Enhanced sampling MD simulations were first performed to explore the influence of C270R mutation on the protease dynamics, and sampled thermodynamically favorable conformations were then submitted to MM/PBSA and QM/MM MD simulations for thorough characterization of the protease-substrate binding and covalent reactions. The disclosed proteolysis mechanism of PL^pro, as characterized by the occurrence of proton transfer from the catalytic C111 to H272 prior to the substrate binding and with deacylation being the rate-determining step of the whole proteolysis process, is not completely identical to that of the 3C-like protease, another key cysteine protease of coronaviruses. The C270R mutation alters the structural dynamics of the BL2 loop that indirectly impairs the catalytic function of H272 and reduces the binding of the substrate with the protease, ultimately showing an inhibitory effect on PL^pro. Together, these results provide a comprehensive understanding at the atomic level of the key aspects of SARS-CoV-2 PL^pro proteolysis, including the catalytic activity allosterically regulated by C270 modification, which is crucial to the follow-up inhibitor design and development.

In Silico Design of New Dual Inhibitors of SARS-CoV-2 MPRO through Ligand- and Structure-Based Methods

The viral main protease is one of the most attractive targets among all key enzymes involved in the life cycle of SARS-CoV-2. Considering its mechanism of action, both the catalytic and dimerization regions could represent crucial sites for modulating its activity. Dual-binding the SARS-CoV-2 main protease inhibitors could arrest the replication process of the virus by simultaneously preventing dimerization and proteolytic activity. To this aim, in the present work, we identified two series' of small molecules with a significant affinity for SARS-CoV-2 M^PRO, by a hybrid virtual screening protocol, combining ligand- and structure-based approaches with multivariate statistical analysis. The Biotarget Predictor Tool was used to filter a large in-house structural database and select a set of benzo[b]thiophene and benzo[b]furan derivatives. ADME properties were investigated, and induced fit docking studies were performed to confirm the DRUDIT prediction. Principal component analysis and docking protocol at the SARS-CoV-2 M^PRO dimerization site enable the identification of compounds 1b,c,i,l and 2i,l as promising drug molecules, showing favorable dual binding site affinity on SARS-CoV-2 M^PRO.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

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.

A Molecular Mechanics Energy Partitioning Software for Biomolecular Systems

The partitioning of the molecular mechanics (MM) energy in calculations involving biomolecular systems is important to identify the source of major stabilizing interactions, e.g., in ligand–protein interactions, or to identify residues with considerable contributions in hybrid multiscale calculations, i.e., quantum mechanics/molecular mechanics (QM/MM). Here, we describe Energy Split, a software program to calculate MM energy partitioning considering the AMBER Hamiltonian and parameters. Energy Split includes a graphical interface plugin for VMD to facilitate the selection of atoms and molecules belonging to each part of the system. Energy Split is freely available at or can be easily installed through the VMD Store.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Allosteric Modulation of the Main Protease (MPro) of SARS-CoV-2 by Casticin—Insights from Molecular Dynamics Simulations

Inhibition of the main protease (M^pro) of SARS-CoV-2 has been suggested to be vital in shutting down viral replication in a host. Most efforts aimed at inhibiting M^Pro activity have been channeled into competitive inhibition at the active site, but this strategy will require a high inhibitor concentration and impressive inhibitor-M^Pro binding affinity. Allosteric inhibition can potentially serve as an effective strategy for alleviating these limitations. In this study, the ability of antiviral natural products to inhibit M^Pro in an allosteric fashion was explored with in silico techniques. Molecular docking revealed a strong interaction between casticin, an antiviral flavonoid, and M^pro at a site distant from the active site. This site, characterized as a distal site, has been shown to have an interdependent dynamic effect with the active site region. M^pro only, M^pro-peptide (binary) and M^pro-peptide-casticin (ternary) complexes were subjected to molecular dynamics simulations for 50 ns to investigate the modulatory activity of casticin binding on M^pro. Molecular dynamic simulations revealed that binding of casticin at the distal site interferes with the proper orientation of the peptide substrate in the oxyanion hole of the active site, and this could lead to a halt or decrease in catalytic activity. This study therefore highlights casticin as a potential allosteric modulator of the SARS-CoV-2 main protease, which could be optimized and developed into a potential lead compound for anti-SARS-CoV-2 chemotherapy.

The critical role of Asp206 stabilizing residues on the catalytic mechanism of the Ideonella sakaiensis PETase

We described the catalytic mechanism of IsPETase, a polyethylene-terephthalate degrading enzyme. The reaction was found to progress in four steps, divided in two events: formation of the first transition intermediate and hydrolysis of the adduct.

Applications of density functional theory in COVID-19 drug modeling

The rapidly evolving Coronavirus 2019 (COVID-19) pandemic has led to millions of deaths around the world, highlighting the pressing need to develop effective antiviral pharmaceuticals. Recent efforts with computer-aided rational drug discovery have allowed detailed examination of drug–macromolecule interactions primarily by molecular mechanics (MM) techniques. Less widely applied in COVID-19 drug modeling is density functional theory (DFT), a quantum mechanics (QM) method that enables electronic structure calculations and elucidations of reaction mechanisms. Here, we review recent advances in applying DFT in molecular modeling studies of COVID-19 pharmaceuticals. We start by providing an overview of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drugs and targets, followed by a brief introduction to DFT. We then provide a discussion of different approaches by which DFT has been applied. Finally, we discuss essential factors to consider when incorporating DFT in future drug modeling research.