Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

Potential (co-)contamination of dairy milk with AFM1 and MC-LR and their synergistic interaction in inducing mitochondrial dysfunction in HepG2 cells

Several toxic metabolites, such as aflatoxin M1 (AFM1), are known to contaminate dairy milk. However, as mentioned in an external EFSA report, there is a knowledge gap regarding the carry-over of certain emerging toxins such as microcystin-LR (MC-LR). Therefore, this work aimed to develop an LC-MS/MS method for MC-LR quantification in dairy milk. Also, the method included AFM1 as a common fungal metabolite and applied to analyze 113 dairy milk samples collected directly after the end of the summer peak. Both toxins were below their LODs, keeping the question on MC-LR carry-over still unanswered. Moreover, an in silico analysis, using a 3D molecular modeling was performed, pointing to a possible interaction between MC-LR and milk proteins, especially β-lactoglobulin. Since AFM1 and MC-LR are hepatotoxic, their interaction in inducing mitochondrial dysfunction in HepG2 cells was investigated at low (subcytotoxic) concentrations. Live cell imaging-based assays showed an inhibition in cell viability, without involvement of caspase-3/7, and a hyperpolarization in the mitochondrial membrane potential after the exposure to a mixture of 100 ng mL-1 AFM1 and 1000 ng mL-1 MC-LR for 48h. Extracellular flux analysis revealed inhibitions of several key parameters of mitochondrial function (basal respiration, ATP-linked respiration, and spare respiratory capacity).

Are protein-ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?

Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.

Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK

Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.

Bioinformatic investigation of Nipah virus surface protein mutations: Molecular docking with Ephrin B2 receptor, molecular dynamics simulation, and structural impact analysis

The SARS-CoV-2 outbreak resulted in significant challenges and loss of life. The Nipah virus, known for its high infectivity and severity, was designated an emergency concern by the World Health Organization. To understand its mutations, the Nipah virus proteins were analyzed extensively, with a focus on the essential G and F proteins responsible for viral entry into host cells. Our bioinformatics analysis unveiled multiple mutations, including simultaneous mutations within a single sequence. Notably, the G273S mutation in the F protein was identified as a potential cause of structural damage, which carries significant implications for vaccine development. Comparing the docking scores of G and F proteins with the Ephrin B2 receptor, it was found that the Y228H mutation in the G protein and the D252G mutation in the F protein likely affect virus entry into host cells. Moreover, our investigation into stability and deformability highlighted the impact of the Y228H mutation in the G protein complex. Molecular dynamics simulations revealed increased flexibility and conformational changes in the G protein complex with the Y228H mutation compared with the known complex. Furthermore, evaluating the root mean square deviation variation demonstrated greater dynamic behavior in the G protein complex and the Ephrin B2 receptor complex. This comprehensive study provides valuable insights into Nipah virus mutations, their significance for vaccine development, and the importance of understanding protein complex behavior in drug discovery. The identified mutations, especially G273S and Y228H, hold crucial implications for future research and potential interventions against the Nipah virus.

Computational Investigations on Reaction Mechanisms of the Covalent Inhibitors Ponatinib and Analogs Targeting the Extracellular Signal-Regulated Kinases

As an important cancer therapeutic target, extracellular signal-regulated kinases (ERK) are involved in triggering various cellular responses in tumors. Regulation of the ERK signaling pathway by the small molecular inhibitors is highly desired for the sake of cancer therapy. In contrast to the routine inhibitors targeting ERKs through long-range non-bonding interactions, Ponatinib, a covalent inhibitor to ERK2 with a macrocyclic structure characterized by the α,β-C=C unsaturated ketone, can form the stable -C(S)-C(H)-type complex via the four-center barrier due to the nucleophilic addition reaction of the thiol group of the Cys166 residue of ERK2 with the C=C double bond of Ponatinib with reaction free-energy barrier of 47.2 kcal/mol. Reaction mechanisms for the covalent binding were calculated using QM/MM methods and molecular dynamics simulations. The interaction modes and the corresponding binding free energies were obtained for the non-covalent and covalent complexation. The binding free energies of the non-covalent and covalent inhibitions are 14.8 kcal/mol and 33.4 kcal/mol, respectively. The mechanistic study stimulated a rational design on the modified Ponatinib structure by substituting the C=C bond with the C=N bond. It was demonstrated that the new compound exhibits better inhibition activity toward ERK2 in term of both thermodynamic and kinetic aspects through the covalent binding with a lower reaction free-energy barrier of 23.1 kcal/mol. The present theoretical work sheds new light on the development of the covalent inhibitors for the regulation of ERKs.

Interaction of SARS-CoV-2 with host cells and antibodies: experiment and simulation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the devastating global COVID-19 pandemic announced by WHO in March 2020. Through unprecedented scientific effort, several vaccines, drugs and antibodies have been developed, saving millions of lives, but the fight against COVID-19 continues as immune escape variants of concern such as Delta and Omicron emerge. To develop more effective treatments and to elucidate the side effects caused by vaccines and therapeutic agents, a deeper understanding of the molecular interactions of SARS-CoV-2 with them and human cells is required. With special interest in computational approaches, we will focus on the structure of SARS-CoV-2 and the interaction of its spike protein with human angiotensin-converting enzyme-2 (ACE2) as a prime entry point of the virus into host cells. In addition, other possible viral receptors will be considered. The fusion of viral and human membranes and the interaction of the spike protein with antibodies and nanobodies will be discussed, as well as the effect of SARS-CoV-2 on protein synthesis in host cells.

Synthesis, Characterization, and DFT Calculations of a New Sulfamethoxazole Schiff Base and Its Metal Complexes

A new Schiff base, 4-((1E,2E)-3-(furan-2-yl)allylidene)amino)-N-(5-methylisoxazol-3-yl) benzene-sulfonamide (L), was synthesized by thermal condensation of 3-(2-furyl)acrolein and sulfamethoxazole (SMX), and the furan Schiff base (L) was converted to a phenol Schiff base (L') according to the Diels-Alder [4 + 2] cycloaddition reaction and studied experimentally. The structural and spectroscopic properties of the Schiff base were also corroborated by utilizing density functional theory (DFT) calculations. Furthermore, a series of lanthanide and transition metal complexes of the Schiff base were synthesized from the nitrate salts of Gd, Sm, Nd, and Zn (L1, L2, L3, and L4), respectively. Various spectroscopic studies confirmed the chemical structures of the Schiff-base ligand and its complexes. Based on the spectral studies, a nine-coordinated geometry was assigned to the lanthanide complexes and a six-coordinated geometry to the zinc complex. The elemental analysis data confirmed the suggested structure of the metal complexes, and the TGA studies confirmed the presence of one coordinated water molecule in the lanthanide complexes and one crystalline water molecule in the zinc complex; in addition, the conductivity showed the neutral nature of the complexes. Therefore, it is suggested that the ligand acts as a bidentate through coordinates to each metal atom by the isoxazole nitrogen and oxygen atoms of the sulfur dioxide moiety of the SMX based on FTIR studies. The ligand and its complexes were tested for their anti-inflammatory, anti-hemolytic, and antioxidant activities by various colorimetric methods. These complexes were found to exhibit potential effects of the selected biological activities.

Diphenyl Diselenide and SARS-CoV-2: in silico Exploration of the Mechanisms of Inhibition of Main Protease (Mpro) and Papain-like Protease (PLpro)

The SARS-CoV-2 pandemic has prompted global efforts to develop therapeutics. The main protease of SARS-CoV-2 (M^pro) and the papain-like protease (PL^pro) are essential for viral replication and are key targets for therapeutic development. In this work, we investigate the mechanisms of SARS-CoV-2 inhibition by diphenyl diselenide (PhSe)₂ which is an archetypal model of diselenides and a renowned potential therapeutic agent. The in vitro inhibitory concentration of (PhSe)₂ against SARS-CoV-2 in Vero E6 cells falls in the low micromolar range. Molecular dynamics (MD) simulations and density functional theory (DFT) calculations [level of theory: SMD-B3LYP-D3(BJ)/6-311G(d,p), cc-pVTZ] are used to inspect non-covalent inhibition modes of both proteases via π-stacking and the mechanism of covalent (PhSe)₂ + M^pro product formation involving the catalytic residue C145, respectively. The in vitro CC₅₀ (24.61 μM) and EC₅₀ (2.39 μM) data indicate that (PhSe)₂ is a good inhibitor of the SARS-CoV-2 virus replication in a cell culture model. The in silico findings indicate potential mechanisms of proteases' inhibition by (PhSe)₂; in particular, the results of the covalent inhibition here discussed for M^pro, whose thermodynamics is approximatively isoergonic, prompt further investigation in the design of antiviral organodiselenides.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

Estimating the binding energetics of reversible covalent inhibitors of the SARS-CoV-2 main protease: an in silico study

The main protease (M^pro) of the SARS-CoV-2 virus is an attractive therapeutic target for developing antivirals to combat COVID-19. M^pro is essential for the replication cycle of the SARS-CoV-2 virus, so inhibiting M^pro blocks a vital piece of the cell replication machinery of the virus. A promising strategy to disrupt the viral replication cycle is to design inhibitors that bind to the active site cysteine (Cys145) of the M^pro. Cysteine targeted covalent inhibitors are gaining traction in drug discovery owing to the benefits of improved potency and extended drug-target engagement. An interesting aspect of these inhibitors is that they can be chemically tuned to form a covalent, but reversible bond, with their targets of interest. Several small-molecule cysteine-targeting covalent inhibitors of the M^pro have been discovered-some of which are currently undergoing evaluation in early phase human clinical trials. Understanding the binding energetics of these inhibitors could provide new insights to facilitate the design of potential drug candidates against COVID-19. Motivated by this, we employed rigorous absolute binding free energy calculations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations to estimate the energetics of binding of some promising reversible covalent inhibitors of the M^pro. We find that the inclusion of enhanced sampling techniques such as replica-exchange algorithm in binding free energy calculations can improve the convergence of predicted non-covalent binding free energy estimates of inhibitors binding to the M^pro target. In addition, our results indicate that binding free energy calculations coupled with multiscale simulations can be a useful approach to employ in ranking covalent inhibitors to their targets. This approach may be valuable in prioritizing and refining covalent inhibitor compounds for lead discovery efforts against COVID-19 and other coronavirus infections.

Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT

The COVID-19 pandemic has accelerated the study of drugs, most notably ivermectin and more recently Paxlovid (PF-07321332) which is in phase III clinical trials with experimental data showing covalent binding to the viral protease Mpro. Theoretical developments of catalytic site-directed docking support thermodynamically feasible non-covalent binding to Mpro. Here we show that Paxlovid binds non-covalently at regions other than the catalytic sites with energies stronger than reported and at the same binding site as the ivermectin B1a homologue, all through theoretical methodologies, including blind docking. We volumetrically characterize the non-covalent interaction of the ivermectin homologues (avermectins B1a and B1b) and Paxlovid with the mMpro monomer, through molecular dynamics and scaled particle theory (SPT). Using the fluctuation-dissipation theorem (FDT), we estimated the electric dipole moment fluctuations at the surface of each of complex involved in this study, with similar trends to that observed in the interaction volume. Using fluctuations of the intrinsic volume and the number of flexible fragments of proteins using anisotropic and Gaussian elastic networks (ANM+GNM) suggests the complexes with ivermectin are more dynamic and flexible than the unbound monomer. In contrast, the binding of Paxlovid to mMpro shows that the mMpro-PF complex is the least structurally dynamic of all the species measured in this investigation. The results support a differential molecular mechanism of the ivermectin and PF homologues in the mMpro monomer. Finally, the results showed that Paxlovid despite beingbound in different sites through covalent or non-covalent forms behaves similarly in terms of its structural flexibility and volumetric behaviour.

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.

In Silico Drug Repositioning to Target the SARS-CoV-2 Main Protease as Covalent Inhibitors Employing a Combined Structure-Based Virtual Screening Strategy of Pharmacophore Models and Covalent Docking

The epidemic caused by the SARS-CoV-2 coronavirus, which has spread rapidly throughout the world, requires urgent and effective treatments considering that the appearance of viral variants limits the efficacy of vaccines. The main protease of SARS-CoV-2 (Mpro) is a highly conserved cysteine proteinase, fundamental for the replication of the coronavirus and with a specific cleavage mechanism that positions it as an attractive therapeutic target for the proposal of irreversible inhibitors. A structure-based strategy combining 3D pharmacophoric modeling, virtual screening, and covalent docking was employed to identify the interactions required for molecular recognition, as well as the spatial orientation of the electrophilic warhead, of various drugs, to achieve a covalent interaction with Cys145 of Mpro. The virtual screening on the structure-based pharmacophoric map of the SARS-CoV-2 Mpro in complex with an inhibitor N3 (reference compound) provided high efficiency by identifying 53 drugs (FDA and DrugBank databases) with probabilities of covalent binding, including N3 (Michael acceptor) and others with a variety of electrophilic warheads. Adding the energy contributions of affinity for non-covalent and covalent docking, 16 promising drugs were obtained. Our findings suggest that the FDA-approved drugs Vaborbactam, Cimetidine, Ixazomib, Scopolamine, and Bicalutamide, as well as the other investigational peptide-like drugs (DB04234, DB03456, DB07224, DB7252, and CMX-2043) are potential covalent inhibitors of SARS-CoV-2 Mpro.

Accelerating the discovery of the beyond rule of five compounds that have high affinities toward SARS-CoV-2 spike RBD

The battle against SARS-CoV-2 coronavirus is the focal point for the global pandemic that has affected millions of lives worldwide. The need for effective and selective therapeutics for the treatment of the disease caused by SARS-CoV-2 is critical. Herein, we performed a hierarchical computational approach incorporating molecular docking studies, molecular dynamics simulations, absolute binding energy calculations, and steered molecular dynamics simulations for the discovery of potential compounds with high affinity towards SARS-CoV-2 spike RBD. By leveraging ZINC15 database, a total of 1282 in-clinical and FDA approved drugs were filtered out from nearly 0.5 million protomers of relatively large compounds (MW > 500, and LogP ≤ 5). Our results depict plausible mechanistic aspects related to the blockage of SARS-CoV-2 spike RBD by the top hits discovered. We found that the most promising candidates, namely, ZINC95628821, ZINC95617623, ZINC3979524, and ZINC261494658, strongly bind to the spike RBD and interfere with the human ACE2 receptor. These findings accelerate the rational design of selective inhibitors targeting the spike RBD protein of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Electrophilic warheads in covalent drug discovery: an overview

INTRODUCTION

Covalent drugs have been used for more than hundred years, but gathered larger interest in the last two decades. There are currently over a 100 different electrophilic warheads used in covalent ligands, and there are several considerations tailoring their reactivity against the target of interest, which is still a challenging task.

AREAS COVERED

This review aims to give an overview of electrophilic warheads used for protein labeling in chemical biology and medicinal chemistry. The warheads are discussed by targeted residues, mechanism and selectivity, and analyzed through three different datasets including our collection of warheads, the CovPDB database, and the FDA approved covalent drugs. Moreover, the authors summarize general practices that facilitate the selection of the appropriate warhead for the target of interest.

EXPERT OPINION

In spite of the numerous electrophilic warheads, only a fraction of them is used in current drug discovery projects. Recent studies identified new tractable residues by applying a wider array of warhead chemistries. However, versatile, selective warheads are not available for all targetable amino acids, hence discovery of new warheads for these residues is needed. Broadening the toolbox of the warheads could result in novel inhibitors even for challenging targets developing with significant therapeutic potential.

Binding mechanism of inhibitors to SARS-CoV-2 main protease deciphered by multiple replica molecular dynamics simulations

The outbreak caused by SARS-CoV-2 has received extensive worldwide attention. As the main protease (M^pro) in SARS-CoV-2 has no human homologues, it is feasible to reduce the possibility of targeting the host protein by accidental drugs. Thus, M^pro has been an attractive target of efficient drug design for anti-SARS-CoV-2 treatment. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), and the molecular mechanics-generalized Born surface area (MM-GBSA) method were integrated together to decipher the binding mechanism of four inhibitors masitinib, O6K, FJC and GQU to M^pro. The results indicate that the binding of four inhibitors clearly affects the structural flexibility and internal dynamics of M^pro along with dihedral angle changes of key residues. The analysis of FELs unveils that the stability in the relative orientation and geometric position of inhibitors to M^pro is favorable for inhibitor binding. Residue-based free energy decomposition reveals that the inhibitor-M^pro interaction networks involving hydrogen bonding interactions and hydrophobic interactions provide significant information for the design of potent inhibitors against M^pro. The hot spot residues including H41, M49, F140, N142, G143, C145, H163, H164, M165, E166 and Q189 identified by computational alanine scanning are considered as reliable targets of clinically available inhibitors inhibiting the activities of M^pro.

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.

Modeling the Binding and Conformational Energetics of a Targeted Covalent Inhibitor to Bruton's Tyrosine Kinase

Targeted covalent inhibitors (TCIs) bind to their targets in both covalent and noncovalent modes, providing exceptionally high affinity and selectivity. These inhibitors have been effectively employed as inhibitors of protein kinases, with Taunton and coworkers (Nat. Chem. Biol. 2015, 11, 525-531) reporting a notable example of a TCI with a cyanoacrylamide warhead that forms a covalent thioether linkage to an active-site cysteine (Cys481) of Bruton's tyrosine kinase (BTK). The specific mechanism of the binding and the relative importance of the covalent and noncovalent interactions is difficult to determine experimentally, and established simulation methods for calculating the absolute binding affinity of an inhibitor cannot describe the covalent bond-forming steps. Here, an integrated approach using alchemical free-energy perturbation and QM/MM molecular dynamics methods was employed to model the complete Gibbs energy profile for the covalent inhibition of BTK by a cyanoacrylamide TCI. These calculations provide a rigorous and complete absolute Gibbs energy profile of the covalent modification binding process. Following a classic thiol-Michael addition mechanism, the target cysteine is deprotonated to form a nucleophilic thiolate, which then undergoes a facile conjugate addition to the electrophilic functional group to form a bond with the noncovalently bound ligand. This model predicts that the formation of the covalent linkage is highly exergonic relative to the noncovalent binding alone. Nevertheless, noncovalent interactions between the ligand and individual amino acid residues in the binding pocket of the enzyme are also essential for ligand binding, particularly van der Waals dispersion forces, which have a larger contribution to the binding energy than the covalent component in absolute terms. This model also shows that the mechanism of covalent modification of a protein occurs through a complex series of steps and that entropy, conformational flexibility, noncovalent interactions, and the formation of covalent linkage are all significant factors in the ultimate binding affinity of a covalent drug to its target.

Characterization of Phytochemicals in Ulva intestinalis L. and Their Action Against SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain

Coronavirus disease-2019 (COVID-19) has caused a severe impact on almost all aspects of human life and economic development. Numerous studies are being conducted to find novel therapeutic strategies to overcome COVID-19 pandemic in a much effective way. Ulva intestinalis L. (Ui), a marine microalga, known for its antiviral property, was considered for this study to determine the antiviral efficacy against severe acute respiratory syndrome-associated Coronavirus-2 (SARS-CoV-2). The algal sample was dried and subjected to ethanolic extraction, followed by purification and analysis using gas chromatography-coupled mass spectrometry (GC-MS). Forty-three known compounds were identified and docked against the S1 receptor binding domain (RBD) of the spike (S) glycoprotein. The compounds that exhibited high binding affinity to the RBD of S1 protein were further analyzed for their chemical behaviour using conceptual density-functional theory (C-DFT). Finally, pharmacokinetic properties and drug-likeliness studies were carried out to test if the compounds qualified as potential leads. The results indicated that mainly phenols, polyenes, phytosteroids, and aliphatic compounds from the extract, such as 2,4-di-tert-butylphenol (2,4-DtBP), doconexent, 4,8,13-duvatriene-1,3-diol (DTD), retinoyl-β-glucuronide 6',3'-lactone (RBGUL), and retinal, showed better binding affinity to the target. Pharmacokinetic validation narrowed the list to 2,4-DtBP, retinal and RBGUL as the possible antiviral candidates that could inhibit the viral spike protein effectively.

Mechanistic and thermodynamic characterization of oxathiazolones as potent and selective covalent immunoproteasome inhibitors

The ubiquitin–proteasome system is responsible for the degradation of proteins and plays a critical role in key cellular processes. While the constitutive proteasome (cPS) is expressed in all eukaryotic cells, the immunoproteasome (iPS) is primarily induced during disease processes, and its inhibition is beneficial in the treatment of cancer, autoimmune disorders and neurodegenerative diseases. Oxathiazolones were reported to selectively inhibit iPS over cPS, and the inhibitory activity of several oxathiazolones against iPS was experimentally determined. However, the detailed mechanism of the chemical reaction leading to irreversible iPS inhibition and the key selectivity drivers are unknown, and separate characterization of the noncovalent and covalent inhibition steps is not available for several compounds. Here, we investigate the chemical reaction between oxathiazolones and the Thr1 residue of iPS by quantum mechanics/molecular mechanics (QM/MM) simulations to establish a plausible reaction mechanism and to determine the rate-determining step of covalent complex formation. The modelled binding mode and reaction mechanism are in line with the selective inhibition of iPS versus cPS by oxathiazolones. The k_inact value of several ligands was estimated by constructing the potential of mean force of the rate-determining step by QM/MM simulations coupled with umbrella sampling. The equilibrium constant K_i of the noncovalent complex formation was evaluated by classical force field-based thermodynamic integration. The calculated K_i and k_inact values made it possible to analyse the contribution of the noncovalent and covalent steps to the overall inhibitory activity. Compounds with similar intrinsic reactivities exhibit varying selectivities for iPS versus cPS owing to subtle differences in the binding modes that slightly affect K_i, the noncovalent affinity, and importantly alter k_inact, the covalent reactivity of the bound compounds. A detailed understanding of the inhibitory mechanism of oxathiazolones is useful in designing iPS selective inhibitors with improved drug-like properties.

Non-conventional interactions of N3 inhibitor with the main protease of SARS-CoV and SARS-CoV-2

The extensive spread of COVID-19 in every continent shows that SARS-CoV-2 virus has a higher transmission rate than SARS-CoV virus which emerged in 2002. This results in a global pandemic that is difficult to control. In this investigation, we analyze the interaction of N3 inhibitor and the main protease of SARS-CoV and SARS-CoV-2 by quantum chemistry calculations. Non-covalent interactions involved in these systems were studied using a model of 469 atoms. Density Functional Theory and Quantum Theory of Atoms in Molecules calculations lead us to the conclusion that non-conventional hydrogen bonds are important to describe attractive interactions in these complexes. The energy of these non-conventional hydrogen bonds represents more than a half of the estimated interaction energy for non-covalent contacts. This means that hydrogen bonds are crucial to correctly describe the bonds between inhibitors and the main proteases. These results could be useful for the design of new drugs, since non-covalent interactions are related to possible mechanisms of action of molecules used against these viruses.

Mechanistic Insight into SARS-CoV-2 Mpro Inhibition by Organoselenides: The Ebselen Case Study

The main protease (Mpro) of SARS-CoV-2 is a current target for the inhibition of viral replication. Through a combined Docking and Density Functional Theory (DFT) approach, we investigated in-silico the molecular mechanism by which ebselen (IUPAC: 2-phenyl-1,2-benzoselenazol-3-one), the most famous and pharmacologically active organoselenide, inhibits Mpro. For the first time, we report on a mechanistic investigation in an enzyme for the formation of the covalent -S-Se- bond between ebselen and a key enzymatic cysteine. The results highlight the strengths and weaknesses of ebselen and provide hints for a rational drug design of bioorganic selenium-based inhibitors.

Benchmarking the Ability of Common Docking Programs to Correctly Reproduce and Score Binding Modes in SARS-CoV-2 Protease Mpro

The coronavirus SARS-CoV-2 main protease, Mpro, is conserved among coronaviruses with no human homolog and has therefore attracted significant attention as an enzyme drug target for COVID-19. The number of studies targeting Mpro for in silico screening has grown rapidly, and it would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. Clearly, current attempts at designing drugs targeting Mpro with the aid of computational docking would benefit from a priori knowledge of the ability of docking programs to predict correct binding modes and score these correctly. In the current work, we tested the ability of several leading docking programs, namely, Glide, DOCK, AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of Mpro ligands in 193 crystal structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of Mpro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction with virtual screening and docking.