Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition

Exploration of novel human neutrophil elastase inhibitors from natural compounds: Virtual screening, in vitro, molecular dynamics simulation and in vivo study

BACKGROUND

Human neutrophil elastase (HNE) is an important contributor to lung diseases such as acute lung injury (ALI) or acute respiratory distress syndrome. Therefore, this study aimed to identify natural HNE inhibitors with anti-inflammatory activity through machine learning algorithms, in vitro assays, molecular dynamic simulation, and an in vivo ALI assay.

METHODS

Based on the optimized Discovery Studio two-dimensional molecular descriptors, combined with different molecular fingerprints, six machine learning models were established using the Naïve Bayesian (NB) method to identify HNE inhibitors. Subsequently, the optimal model was utilized to screen 6925 drug-like compounds obtained from the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform (TCMSP), followed by ADMET analysis. Finally, 10 compounds with reported anti-inflammatory activity were selected to determine their inhibitory activities against HNE in vitro, and the compounds with the best activity were selected for a 100 ns molecular dynamics simulation and its anti-inflammatory effect was evaluated using Poly (I:C)-induced ALI model.

RESULTS

The evaluation of the in vitro HNE inhibition efficiency of the 10 selected compounds showed that the flavonoid tricetin had the strongest inhibitory effect on HNE. The molecular dynamics simulation indicated that the binding of tricetin to HNE was relatively stable throughout the simulation. Importantly, in vivo experiments indicated that tricetin treatment substantially improved the Poly (I:C)-induced ALI.

CONCLUSION

The proposed NB model was proved valuable for exploring novel HNE inhibitors, and natural tricetin was screened out as a novel HNE inhibitor, which was confirmed by in vitro and in vivo assays for its inhibitory activities.

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.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

Enterocin DD14 can inhibit the infection of eukaryotic cells with enveloped viruses

Bacteriocins are ribosomally synthesized bacterial peptides endowed with antibacterial, antiprotozoal, anticancer and antiviral activities. In the present study, we evaluated the antiviral activities of two bacteriocins, enterocin DD14 (EntDD14) and lacticaseicin 30, against herpes simplex virus type 1 (HSV-1), human coronavirus 229E (HCoV-229E) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Vero, Huh7 and Vero E6 cells, respectively. In addition, the interactions of these bacteriocins with the envelope glycoprotein D of HSV-1 and the receptor binding domains of HCoV-229E and SARS-CoV-2 have been computationally evaluated using protein-protein docking and molecular dynamics simulations. HSV-1 replication in Vero cells was inhibited by EntDD14 and, to a lesser extent, by lacticaseicin 30 added to cells after virus inoculation. EntDD14 and lacticaseicin 30 had no apparent antiviral activity against HCoV-229E; however, EntDD14 was able to inhibit SARS-CoV-2 in Vero E6 cells. Further studies are needed to elucidate the antiviral mechanism of these bacteriocins.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 Mpro Inhibitors from Large Chemical Libraries

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

Cracking a cancer code DNA methylation in epigenetic modification: an in-silico approach on efficacy assessment of Sri Lanka-oriented nutraceuticals

DNA methyltransferase (DNMTs) are essential epigenetic modifiers that play a critical role in gene regulation. These enzymes add a methyl group to cytosine's 5'-carbon, specifically within CpG dinucleotides, using S-adenosyl-L-methionine. Abnormal overexpression of DNMTs can alter the gene expression patterns and contribute to cancer development in the human body. Therefore, the inhibition of DNMT is a promising therapeutic approach to cancer treatment. This study was aimed to identify potential nutraceutical inhibitors from the Sri Lanka Flora database using computational methods, which provided an atomic-level description of the drug binding site and examined the interactions between nutraceuticals and amino acids of the DNMT enzyme. A series of nutraceuticals from Sri Lanka-oriented plants were selected and evaluated to assess their inhibitory effects on DNMT using absorption, distribution, metabolism, excretion and toxicity analysis, virtual screening, molecular docking, molecular dynamics simulation and trajectory analysis. Azacitidine, a DNMT inhibitor approved by the US Food and Drug Administration, was selected as a reference inhibitor. The complexes with more negative binding energies were selected and further assessed for their potency. Seven molecules were identified from 200 nutraceuticals, demonstrating significantly negative binding energies against the DNMT enzyme. Various trajectory analyses were conducted to investigate the stability of the DNMT enzyme. The results indicated that petchicine (NP#0003), ouregidione (NP#0011) and azacitidine increased the stability of the DNMT enzyme. Consequently, these two nutraceuticals showed inhibitory efficacies similar to azacitidine, making them potential candidates for therapeutic interventions targeting DNMT enzyme-related cancers. Additional bioassay testing is recommended to confirm the efficacies of these nutraceuticals and explore their applicability in clinical treatments.Communicated by Ramaswamy H. Sarma.

Identification of SARS-CoV-2 Main Protease Inhibitors Using Chemical Similarity Analysis Combined with Machine Learning

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a-c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

The Potential of Usnic-Acid-Based Thiazolo-Thiophenes as Inhibitors of the Main Protease of SARS-CoV-2 Viruses

Although the COVID-19 pandemic caused by SARS-CoV-2 viruses is officially over, the search for new effective agents with activity against a wide range of coronaviruses is still an important task for medical chemists and virologists. We synthesized a series of thiazolo-thiophenes based on (+)- and (-)-usnic acid and studied their ability to inhibit the main protease of SARS-CoV-2. Substances containing unsubstituted thiophene groups or methyl- or bromo-substituted thiophene moieties showed moderate activity. Derivatives containing nitro substituents in the thiophene heterocycle-just as pure (+)- and (-)-usnic acids-showed no anti-3CLpro activity. Kinetic parameters of the most active compound, (+)-3e, were investigated, and molecular modeling of the possible interaction of the new thiazolo-thiophenes with the active site of the main protease was carried out. We evaluated the binding energies of the ligand and protein in a ligand-protein complex. Active compound (+)-3e was found to bind with minimum free energy; the binding of inactive compound (+)-3g is characterized by higher values of minimum free energy; the positioning of pure (+)-usnic acid proved to be unstable and is accompanied by the formation of intermolecular contacts with many amino acids of the catalytic binding site. Thus, the molecular dynamics results were consistent with the experimental data. In an in vitro antiviral assay against six strains (Wuhan, Delta, and four Omicron sublineages) of SARS-CoV-2, (+)-3e demonstrated pronounced antiviral activity against all the strains.

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.

Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 Mpro inhibitors

The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.

Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV

The main protease (Mpro) of SARS-CoV-2 is essential for viral replication, which suggests that the Mpro is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 Mpro in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 Mpro in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 Mpro in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 Mpro enzymatic activity, with a half maximal inhibitory concentration (IC50) of 2.69 μM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. Mpro-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 Mpro enzymatic activity, with an IC50 of 3.05 μM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC50 of <3.5 μM. Thus, C1 shows potential as an effective Mpro inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 Mpro and MERS-CoV Mpro.

Inhibitor Trapping in N-Myristoyltransferases as a Mechanism for Drug Potency

Predicting inhibitor potency is critical in drug design and development, yet it has remained one of computational biology's biggest unresolved challenges. Here, we show that in the case of the N-myristoyltransferase (NMT), this problem could be traced to the mechanisms by which the NMT enzyme is inhibited. NMT adopts open or closed conformations necessary for orchestrating the different steps of the catalytic process. The results indicate that the potency of the NMT inhibitors is determined by their ability to stabilize the enzyme conformation in the closed state, and that in this state, the small molecules themselves are trapped and locked inside the structure of the enzyme, creating a significant barrier for their dissociation. By using molecular dynamics simulations, we demonstrate that the conformational stabilization of the protein molecule in its closed form is highly correlated with the ligands activity and can be used to predict their potency. Hence, predicting inhibitor potency in silico might depend on modeling the conformational changes of the protein molecule upon binding of the ligand rather than estimating the changes in free binding energy that arise from their interaction.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Structural Modifications Introduced by NS2B Cofactor Binding to the NS3 Protease of the Kyasanur Forest Disease Virus

Kyasanur Forest Disease virus (KFDV), a neglected human pathogenic virus, is a Flavivirus that causes severe hemorrhagic fever in humans. KFDV is transmitted to humans by the bite of the hard tick (Haemaphysalis spinigera), which acts as a reservoir of KFDV. The recent expansion of the endemic area of KFDV is of concern and requires the development of new preventive measures against KFDV. Currently, there is no antiviral therapy against KFDV, and the existing vaccine has limited efficacy. To develop a new antiviral therapy against KFDV, we focused on the nonstructural proteins NS2B and NS3 of KFDV, which are responsible for serine protease activity. Viral proteases have shown to be suitable therapeutic targets in the development of antiviral drugs against many diseases. However, success has been limited in flaviviruses, mainly because of the important features of the active site, which is flat and highly charged. In this context, the present study focuses on the dynamics of NS2B and NS3 to identify potential allosteric sites in the NS2B/NS3 protease of KDFV. To our knowledge, there are no reports on the dynamics of NS2B and NS3 in KFDV, and the crystal structure of the NS2B/NS3 protease of KFDV has not yet been solved. Overall, we created the structure of the NS2B/NS3 protease of KFDV using AlphaFold and performed molecular dynamics simulations with and without NS2B cofactor to investigate structural rearrangements due to cofactor binding and to identify alternative allosteric sites. The identified allosteric site is promising due to its geometric and physicochemical properties and druggability and can be used for new drug development. The applicability of the proposed allosteric binding sites was verified for the best-hit molecules from the virtual screening and MD simulations.

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC₅₀ ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC₅₀ = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC₅₀ 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

Repurposing of FDA-approved drugs as dual-acting MAO-B and AChE inhibitors against Alzheimer's disease: An in silico and in vitro study

An in silico consensus molecular docking approach and in vitro evaluations were adopted in the present study to explore a dataset of FDA-approved drugs as novel multitarget MAO-B/AChE agents in the treatment of Alzheimer's disease (AD). GOLD 5.3 and Glide were employed in the virtual assessments and consensus superimpositions of the obtained poses were applied to increase the reliability of the docking protocols. Furthermore, the top ranked molecules were subjected to binding free energy calculations using MM/GBSA, Induced fit docking (IFD) simulations, and a literature review. Consequently, the top four multitarget drugs were examined for their in vitro MAO-B and AChE inhibition effects. The consensus molecular docking identified Dolutegravir, Rebamipide, Loracarbef and Diflunisal as potential multitarget drugs. The biological data demonstrated that most of the docking scores were in good correlation with the in vitro experiments, however the theoretical simulations in the active site of MAO-B identified two false-positives - Rebamipide and Diflunisal. Dolutegravir and Loracarbef were accessed as active MAO-B inhibitors, while Dolutegravir, Rebamapide and Diflunisal as potential AChE inhibitors. The antiretroviral agent Dolutegravir exhibited the most potent multitarget activity - 41% inhibition of MAO-B (1 μM) and 68% inhibition of AChE (10 μM). Visualizations of the intermolecular interactions of Dolutegravir in the active sites of MAO-B and AChE revealed the formation of several stable hydrogen bonds. Overall, Dolutegravir was identified as a potential anti-AD drug, however further in vivo evaluations should be considered.

An in-solution snapshot of SARS-COV-2 main protease maturation process and inhibition

The main protease from SARS-CoV-2 (Mpro) is responsible for cleavage of the viral polyprotein. Mpro self-processing is called maturation, and it is crucial for enzyme dimerization and activity. Here we use C145S Mpro to study the structure and dynamics of N-terminal cleavage in solution. Native mass spectroscopy analysis shows that mixed oligomeric states are composed of cleaved and uncleaved particles, indicating that N-terminal processing is not critical for dimerization. A 3.5 Å cryo-EM structure provides details of Mpro N-terminal cleavage outside the constrains of crystal environment. We show that different classes of inhibitors shift the balance between oligomeric states. While non-covalent inhibitor MAT-POS-e194df51-1 prevents dimerization, the covalent inhibitor nirmatrelvir induces the conversion of monomers into dimers, even with intact N-termini. Our data indicates that the Mpro dimerization is triggered by induced fit due to covalent linkage during substrate processing rather than the N-terminal processing.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC₅₀ values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC₅₀ of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Structure-Based Virtual Screening and Functional Validation of Potential Hit Molecules Targeting the SARS-CoV-2 Main Protease

The recent global health emergency caused by the coronavirus disease 2019 (COVID-19) pandemic has taken a heavy toll, both in terms of lives and economies. Vaccines against the disease have been developed, but the efficiency of vaccination campaigns worldwide has been variable due to challenges regarding production, logistics, distribution and vaccine hesitancy. Furthermore, vaccines are less effective against new variants of the SARS-CoV-2 virus and vaccination-induced immunity fades over time. These challenges and the vaccines' ineffectiveness for the infected population necessitate improved treatment options, including the inhibition of the SARS-CoV-2 main protease (M^pro). Drug repurposing to achieve inhibition could provide an immediate solution for disease management. Here, we used structure-based virtual screening (SBVS) to identify natural products (from NP-lib) and FDA-approved drugs (from e-Drug3D-lib and Drugs-lib) which bind to the M^pro active site with high-affinity and therefore could be designated as potential inhibitors. We prioritized nine candidate inhibitors (e-Drug3D-lib: Ciclesonide, Losartan and Telmisartan; Drugs-lib: Flezelastine, Hesperidin and Niceverine; NP-lib: three natural products) and predicted their half maximum inhibitory concentration using DeepPurpose, a deep learning tool for drug-target interactions. Finally, we experimentally validated Losartan and two of the natural products as in vitro M^pro inhibitors, using a bioluminescence resonance energy transfer (BRET)-based M^pro sensor. Our study suggests that existing drugs and natural products could be explored for the treatment of COVID-19.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

In silico investigation of the therapeutic and prophylactic potential of medicinal substances bearing guanidine moieties against COVID-19

The current viral pandemic, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), creates health, mental, economic, and other serious challenges that are better to say global crisis. Despite the existence of successful vaccines, the possible mutations which can lead to the born of novel and possibly more dangerous variants of the virus as well as the absence of definitive treatment for this potentially fatal multiple-organ infection in critically ill patients make us keep searching. Theoretically targeting human and viral receptors and enzymes via molecular docking and dynamics simulations can be considered a wise, rational, and efficient way to develop therapeutic agents against COVID-19. In this way, The RNA-dependent RNA polymerase (RdRP), main protease, and spike glycoprotein of SARS-CoV-2 as well as the human angiotensin-converting enzyme 2 receptor and transmembrane serine protease 2 are the most discussed and studied targets that play essential roles in the viral life and infection cycle. In the current in silico investigation, the guanidine functionality containing drugs and medicinal substances such as metformin, famotidine, neuraminidase inhibitors, antimalarial medications, anticancer drug imatinib, CGP compounds, and human serine protease inhibitor camostat were studied against the above-mentioned therapeutic targets and most of them (especially imatinib) have revealed an incredible spectrum of free docking scores and MD results. The current in silico investigation that its novel perspective of view is corroborated by the different experimental and clinical evaluations, confirms that the guanidine moiety can be considered as a missing promising pharmacophore in drug design and development approaches against SARS-CoV-2. Considering the chemical potency of this polyamine group in chemical interaction creation, the observed outcomes in this virtual screening were not surprising. On the other hand, the guanidine functional group has unique physico-chemical properties such as basicity that can make the target cells intracellular pH undesirable for the virus entry, uncoating, and cytosolic lifecycle. According to the obtained results in the current study that are interestingly confirmed by the previously reported efficacy of some the guanidine carrying drugs in COVID-19, guanidine as a potential multi-target anti-SARS-CoV-2 functional scaffold deserves further comprehensive investigations.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02528-y.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction

The COVID-19 pandemic has resulted in millions of deaths around the world. Multiple vaccines are in use, but there are many underserved locations that do not have adequate access to them. Variants may emerge that are highly resistant to existing vaccines, and therefore cheap and readily obtainable therapeutics are needed. Phytochemicals, or plant chemicals, can possibly be such therapeutics. Phytochemicals can be used in a polypharmacological approach, where multiple viral proteins are inhibited and escape mutations are made less likely. Finding the right phytochemicals for viral protein inhibition is challenging, but in-silico screening methods can make this a more tractable problem. In this study, we screen a wide range of natural drug products against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow. This workflow consists of a structure-based virtual screening (SBVS), where an initial phytochemical library was docked against all selected protein structures. Subsequently, ligand-based virtual screening (LBVS) was employed, where chemical features of 34 lead compounds obtained from the SBVS were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. A computational docking validation of the 53 predicted leads obtained from LBVS revealed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Collectively, this study demonstrates the advantage of incorporating machine learning elements into a virtual screening workflow.Communicated by Ramaswamy H. Sarma.

Interaction of panduratin A and derivatives with the SARS-CoV-2 main protease (mpro): a molecular docking study

Panduratin A (Pa-A) is a prenylated cyclohexenyl chalcone isolated from the rhizomes of the medicinal and culinary plant Boesenbergia rotunda (L.) Mansf., commonly called fingerroots. Both an ethanolic plant extract and Pa-A have shown a marked antiviral activity against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic disease. Pa-A functions as a protease inhibitor inhibiting infection of human cells by the virus. We have modeled the interaction of Pa-A, and 26 panduratin analogues with the main protease (M^pro) of SARS-CoV-2 using molecular docking. The natural product 4-hydroxypanduratin showed a higher M^pro binding capacity than Pa-A and isopanduratin A. The interaction with M^Pro of all known panduratin derivatives (Pa-A to Pa-Y) have been compared, together with more than 60 reference products. Three compounds emerged as potential robust M^Pro binders: Pa-R, Pa-V, Pa-S, with a binding capacity significantly higher than 4-OH-Pa-A and Pa-A. The empirical energy of interaction (ΔE) calculated with the best compound in the panduratin series, Pa-R bound to M^pro, surpassed that measured with the top reference protease inhibitors such a ruprintrivir, lufotrelvir, and glecaprevir. Structure-binding relationships are discussed. Compounds with a flavanone moiety (PA-R/S) are the best binders, better than those with a chromene unit (Pa-F/G). The extended molecules (such as Pa-V) exhibit good M^pro binding, but the dimeric compound Pa-Y is too long and protrudes outside the binding cavity. The work provides novel ideas to guide the design of new molecules interacting with M^pro.Communicated by Ramaswamy H. Sarma.

Molecular dynamics and docking studies on potentially active natural phytochemicals for targeting SARS-CoV-2 main protease

In the present study, we screened eighty seven novel phytochemical compounds from four popular herbs, such as, Aegle Marmelos, Coleus Amboinicus, Aerva Lanata and Biophytum Sensitivum and identified the best three for targeting the main protease (M^pro) receptor of SARS-CoV-2. After categorizing all the phytochemicals based upon LibDock scores and hydrogen bonding interactions, the top ranked 26 compounds were further subjected for detailed Molecular dynamics (MD) study. From these screening we identified that Aegelinosides B leads the list with a high LibDock value of 142.50 (binding energy: -8.54 kcal/mol), which is better than several popular reference compounds namely, Tipranavir (LibDock score, 141.50), Saquinavir (125.34), Zopicole (122.9), Pirenepine (122.70), (115.37), Metixene (109.18), Oxiconazole Pimozide (138.00) and Rimonabant (91.88). Detailed analysis for structural stability (RMSD), Cα fluctuations (RMSF), intermolecular hydrogen bond interactions, effect of solvent accessibility (SASA) and compactness (Rg) factors were performed for the best six compounds and it is found that they are very stable and exhibit folding behavior. Apart from the docking and MD tests, through further drug-likeness and toxicity tests, three compounds, such as, Aegelinosides B, Epicatechin, and Feruloyltyramine (LibDock scores, respectively, 142.50, 124.33 and 129.06) can be suggested for fighting SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

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.

Fullerenes against COVID-19: Repurposing C60 and C70 to Clog the Active Site of SARS-CoV-2 Protease

The persistency of COVID-19 in the world and the continuous rise of its variants demand new treatments to complement vaccines. Computational chemistry can assist in the identification of moieties able to lead to new drugs to fight the disease. Fullerenes and carbon nanomaterials can interact with proteins and are considered promising antiviral agents. Here, we propose the possibility to repurpose fullerenes to clog the active site of the SARS-CoV-2 protease, Mpro. Through the use of docking, molecular dynamics, and energy decomposition techniques, it is shown that C60 has a substantial binding energy to the main protease of the SARS-CoV-2 virus, Mpro, higher than masitinib, a known inhibitor of the protein. Furthermore, we suggest the use of C70 as an innovative scaffold for the inhibition of SARS-CoV-2 Mpro. At odds with masitinib, both C60 and C70 interact more strongly with SARS-CoV-2 Mpro when different protonation states of the catalytic dyad are considered. The binding of fullerenes to Mpro is due to shape complementarity, i.e., vdW interactions, and is aspecific. As such, it is not sensitive to mutations that can eliminate or invert the charges of the amino acids composing the binding pocket. Fullerenic cages should therefore be more effective against the SARS-CoV-2 virus than the available inhibitors such as masinitib, where the electrostatic term plays a crucial role in the binding.

Discovery of C-12 dithiocarbamate andrographolide analogues as inhibitors of SARS-CoV-2 main protease: In vitro and in silico studies

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Andrographolide analogues were found to inhibit SARS-CoV-2 main protease.

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The compounds 3k, 3l, 3m and 3t showed promising in vitro inhibitory activity.

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Most of the candidates could bind well to the SARS-CoV-2 main protease active site.

A global crisis of coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has impacted millions of people’s lives throughout the world. In parallel to vaccine development, identifying potential antiviral agents against SARS-CoV-2 has become an urgent need to combat COVID-19. One of the most attractive drug targets for discovering anti-SARS-CoV-2 agents is the main protease (M^pro), which plays a pivotal role in the viral life cycle. This study aimed to elucidate a series of twenty-one 12-dithiocarbamate-14-deoxyandrographolide analogues as SARS-CoV-2 M^pro inhibitors using in vitro and in silico studies. These compounds were initially screened for the inhibitory activity toward SARS-CoV-2 M^pro by in vitro enzyme-based assay. We found that compounds 3k, 3l, 3m and 3t showed promising inhibitory activity against SARS-CoV-2 M^pro with >50% inhibition at 10 μM. Afterward, the binding mode of each compound in the active site of SARS-CoV-2 M^pro was explored by molecular docking. The optimum docked complexes were then chosen and subjected to molecular dynamic (MD) simulations. The MD results suggested that all studied complexes were stable along the simulation time, and most of the compounds could fit well with the SARS-CoV-2 M^pro active site, particularly at S1, S2 and S4 subsites. The per-residue decomposition free energy calculations indicated that the hot-spot residues essential for ligand binding were T25, H41, C44, S46, M49, C145, H163, M165, E166, L167, D187, R188, Q189 and T190. Therefore, the obtained information from the combined experimental and computational techniques could lead to further optimization of more specific and potent andrographolide analogues toward SARS-CoV-2 M^pro.

A Review of the Current Landscape of SARS-CoV-2 Main Protease Inhibitors: Have We Hit the Bullseye Yet?

In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.

Epicatechin is a promising novel inhibitor of SARS-CoV-2 entry by disrupting interactions between angiotensin-converting enzyme type 2 and the viral receptor binding domain: A computational/simulation study

Angiotensin-converting enzyme 2 (ACE2) is the first target of SARS-CoV-2 and a key functional host receptor through which this virus hooks into and infects human cells. The necessity to block this receptor is one of the essential means to prevent the outbreak of COVID-19. This study was conducted to determine the most eligible natural compound to suppress ACE2 to counterfeit its interaction with the viral infection. To do this, the most known compounds of sixty-six Iraqi medicinal plants were generated and retrieved from PubChem database. After preparing a library for Iraqi medicinal plants, 3663 unique ligands’ conformers were docked to ACE2 using the GLIDE tool. Results found that twenty-three compounds exhibited the highest binding affinity with ACE2. The druglikeness and toxicity potentials of these compounds were evaluated using SwissADME and Protox servers respectively. Out of these virtually screened twenty-three compounds, epicatechin and kempferol were predicted to exert the highest druglikeness and lowest toxicity potentials. Extended Molecular dynamics (MD) simulations showed that ACE2-epicatechin complex exhibited a slightly higher binding stability than ACE2-kempferol complex. In addition to the well-known ACE2 inhibitors that were identified in previous studies, this study revealed for the first time that epicatechin from Hypericum perforatum provided a better static and dynamic inhibition for ACE2 with highly favourable pharmacokinetic properties than the other known ACE2 inhibiting compounds. This study entailed the ability of epicatechin to be used as a potent natural inhibitor that can be used to block or at least weaken the SARS-CoV-2 entry and its subsequent invasion. In vitro experiments are required to validate epicatechin effectiveness against the activity of the human ACE2 receptor.