Ensemble Docking Coupled to Linear Interaction Energy Calculations for Identification of Coronavirus Main Protease (3CLpro) Non-Covalent Small-Molecule Inhibitors

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.

Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor

The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 μM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.

Prospecting native and analogous peptides with anti-SARS-CoV-2 potential derived from the trypsin inhibitor purified from tamarind seeds

The study aimed to prospect in silico native and analogous peptides with anti-SARS-CoV-2 potential derived from the trypsin inhibitor purified from tamarind seeds (TTIp). From the most stable theoretical model of TTIp (TTIp 56/287), in silico cleavage was performed for the theoretical identification of native peptides and generation of analogous peptides. The anti-SARS-CoV-2 potential was investigated through molecular dynamics (MD) simulation between the peptides and binding sites of transmembrane serine protease 2 (TMPRSS2), responsible for the entry of SARS-CoV-2 into the host cell. Five native and analogous peptides were obtained and validated through chemical and physical parameters. The best interaction potential energy (IPE) occurred between TMPRSS2 and one of the native peptides obtained by cleavage with trypsin and its analogous peptide. Thus, both peptides showed many hydrophobic residues, a common physical-chemical property among the peptides that inhibit the entry of enveloped viruses, such as SARS-CoV-2, present in specific drugs to treat COVID-19.

A benzimidazole scaffold as a promising inhibitor against SARS-CoV-2

The manuscript reports the green-chemical synthesis of a new diindole-substituted benzimidazole compound, B1 through a straightforward route in coupling between indolyl-3-carboxaldehyde and o-phenylenediamine in water medium under the aerobic condition at 75 ºC. The single crystal X-ray structural analysis of B1 suggests that the disubstituted benzimidazole compound crystallizes in a monoclinic system and the indole groups exist in a perpendicular fashion with respect to benzimidazole moiety. The SARS-CoV-2 screening activity has been studied against 1 × 10e4 VeroE6 cells in a dose-dependent manner following Hoechst 33342 and nucleocapsid staining activity with respect to remdesivir. The compound exhibits 92.4% cell viability for 30 h and 35.1% inhibition against VeroE6 cells at non-cytotoxic concentration. Molecular docking studies predict high binding propensities of B1 with the main protease (M^pro) and non-structural (nsp2 and nsp7-nsp8) proteins of SARS-CoV-2 through a number of non-covalent interactions. Molecular dynamics (MD) simulation analysis for 100 ns confirms the formation of stable conformations of B1-docked proteins with significant changes of binding energy, attributing the potential inhibition properties of the synthetic benzimidazole scaffold against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Robust anti-inflammatory activity of genistein against neutrophil elastase: a microsecond molecular dynamics simulation study

Human Neutrophil Elastase (HNE) is one of the major causes of tissue destruction in numerous chronic and inflammatory disorders and has been reported as a therapeutic target for inflammatory diseases. Overexpression of this enzyme plays a critical role in the pathogenesis of rheumatoid arthritis (RA). The focus of this study is to identify potent natural inhibitors that could target the active site of the HNE through the use of computational methods. The molecular structure of small molecules was retrieved from several natural compound databases. This was followed by structure-based virtual screening, molecular docking, ADMET property predictions and molecular dynamic simulation studies to screen potential HNE inhibitors. In total, 1881 natural compounds were extracted and subjected to molecular docking studies, and 10 compounds were found to have good interactions, exhibiting the best docking scores. Genistein showed higher binding efficacy (-10.28 Kcal/mol) to HNE in comparison to other natural compounds. The conformational stability of the docked complex of the ELANE gene (HNE) with genistein was assessed using 1-microsecond molecular dynamic simulation (MDs), which reliably revealed the unique stereochemical alteration of the complex, indicating its conformational stability and flexibility. Alterations in the enzyme structure upon complex formation were further characterized through clustering analysis and linear interaction energy (LIE) calculation. The outcomes of this research propose novel potential candidates against target HNE.Communicated by Ramaswamy H. Sarma.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

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.

The inhibitory activity of methoxyl flavonoids derived from Inula britannica flowers on SARS-CoV-2 3CLpro

In our ongoing efforts to identify effective natural antiviral agents, four methoxy flavonoids (1-4) were isolated from the Inula britannica flower extract. Their structures were elucidated using nuclear magnetic resonance. Flavonoids 1-4 exhibited inhibitory activity against SARS- CoV-2 3CLpro with IC₅₀ values of 41.6 ± 2.5, 35.9 ± 0.9, 32.8 ± 1.2, and 96.6 ± 3.4 μM, respectively. Flavonoids 1-3 inhibited 3CLpro in a competitive manner. Based on molecular simulations, key amino acids that form hydrogen bond with inhibitor 3 were identified. Finally, we found that inhibitors (1-3) suppressed HCoV-OC43 coronavirus proliferation at micromole concentrations.

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01−M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.

Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery

The lack of biologically relevant protein structures can hinder rational design of small molecules to target G protein-coupled receptors (GPCRs). While ensemble docking using multiple models of the protein target is a promising technique for structure-based drug discovery, model clustering and selection still need further investigations to achieve both high accuracy and efficiency. In this work, we have developed an original ensemble docking approach, which identifies the most relevant conformations based on the essential dynamics of the protein pocket. This approach is applied to the study of small-molecule antagonists for the PAC1 receptor, a class B GPCR and a regulator of stress. As few as four representative PAC1 models are selected from simulations of a homology model and then used to screen three million compounds from the ZINC database and 23 experimentally validated compounds for PAC1 targeting. Our essential dynamics ensemble docking (EDED) approach can effectively reduce the number of false negatives in virtual screening and improve the accuracy to seek potent compounds. Given the cost and difficulties to determine membrane protein structures for all the relevant states, our methodology can be useful for future discovery of small molecules to target more other GPCRs, either with or without experimental structures.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME)

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Investigating novel thiazolyl-indazole derivatives as scaffolds for SARS-CoV-2 MPro inhibitors

COVID-19 is a global pandemic caused by infection with the SARS-CoV-2 virus. Remdesivir, a SARS-CoV-2 RNA polymerase inhibitor, is the only drug to have received widespread approval for treatment of COVID-19. The SARS-CoV-2 main protease enzyme (MPro), essential for viral replication and transcription, remains an active target in the search for new treatments. In this study, the ability of novel thiazolyl-indazole derivatives to inhibit MPro is evaluated. These compounds were synthesized via the heterocyclization of phenacyl bromide with (R)-carvone, (R)-pulegone and (R)-menthone thiosemicarbazones. The binding affinity and binding interactions of each compound were evaluated through Schrödinger Glide docking, AMBER molecular dynamics simulations, and MM-GBSA free energy estimation, and these results were compared with similar calculations of MPro binding various 5-mer substrates (VKLQA, VKLQS, VKLQG) and a previously identified MPro tight-binder X77. From these simulations, we can see that binding is driven by residue specific interactions such as π-stacking with His41, and S/π interactions with Met49 and Met165. The compounds were also experimentally evaluated in a MPro biochemical assay and the most potent compound containing a phenylthiazole moiety inhibited protease activity with an IC50 of 92.9 ​μM. This suggests that the phenylthiazole scaffold is a promising candidate for the development of future MPro inhibitors.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

Casiopeinas® as SARS-CoV-2 main protease (Mpro) inhibitors: a combined DFT, molecular docking and ONIOM approach

Computational combined protocols suggest that Casiopeinas can block the active site of Mpro SARS-CoV-2 by binding to its main amino acids.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Repurposing of Drugs for SARS-CoV-2 Using Inverse Docking Fingerprints

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

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

This review makes a critical evaluation of 61 peer‐reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS‐CoV‐2 M‐pro (M‐pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS‐CoV‐2 M‐pro inhibitors extracted from the literature and a second set of 81 M‐pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS‐CoV‐2 M‐pro inhibitor. Using two SARS‐CoV‐2 M‐pro structures and five protein‐ligand docking programs, we proved that the correlation between the pIC₅₀ and docking scores is not good. Neither was any correlation found between the pIC₅₀ and the ∆G calculated with an MM‐GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M‐pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M‐pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS‐CoV‐2 M‐pro inhibitors presented here could be used for validating future VS protocols which aim to predict M‐pro inhibitors.

Potential Novel Thioether-Amide or Guanidine-Linker Class of SARS-CoV-2 Virus RNA-Dependent RNA Polymerase Inhibitors Identified by High-Throughput Virtual Screening Coupled to Free-Energy Calculations

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new pathogen from the family of Coronaviridae that caused a global pandemic of COVID-19 disease. In the absence of effective antiviral drugs, research of novel therapeutic targets such as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) becomes essential. This viral protein is without a human counterpart and thus represents a unique prospective drug target. However, in vitro biological evaluation testing on RdRp remains difficult and is not widely available. Therefore, we prepared a database of commercial small-molecule compounds and performed an in silico high-throughput virtual screening on the active site of the SARS-CoV-2 RdRp using ensemble docking. We identified a novel thioether-amide or guanidine-linker class of potential RdRp inhibitors and calculated favorable binding free energies of representative hits by molecular dynamics simulations coupled with Linear Interaction Energy calculations. This innovative procedure maximized the respective phase-space sampling and yielded non-covalent inhibitors representing small optimizable molecules that are synthetically readily accessible, commercially available as well as suitable for further biological evaluation and mode of action studies.

Molecular docking identification for the efficacy of natural limonoids against COVID-19 virus main protease

COVID-19 pandemic is the biggest public health problem of the century so far.The main protease (Mpro) is one of the main enzymes studied as a pharmacological target. In this context, the present work aimed to perform a virtual screening of possible inhibitors against the enzyme Mpro, having limonoids as the main object of research as supposed inhibitors. Molecular docking simulations indicated that limonoids have an affinity to complex with M-pro.However, Limonine and Nimoliciol showed nonspecific and low affinity interactions. In conclusion, Limonoids are substances of natural origin that can be used in the study of new pharmacological tools designed to combat and understand COVID-19.

Multifunctional inhibitors of SARS-CoV-2 by MM/PBSA, essential dynamics, and molecular dynamic investigations

The ongoing COVID-19 pandemic demands a novel approach to combat and identify potential therapeutic targets. The SARS-CoV-2 infection causes a hyperimmune response followed by a spectrum of diseases. Limonoids are a class of triterpenoids known to prevent the release of IL-6, IL-15, IL-1α, IL-1β via TNF and are also known to modulate PI3K/Akt/GSK-3β, JNK1/2, MAPKp38, ERK1/2, and PI3K/Akt/mTOR signaling pathways and could help to avoid viral infection, persistence, and pathogenesis. The present study employs a computational approach of virtual screening and molecular dynamic (MD) simulations of such compounds against RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), and Papain-like protease (PLpro) of SARS-CoV-2. MD simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA), and Essential dynamics revealed that the macromolecule-ligand complexes are stable with very low free energy of binding. Such compounds that could modulate both host responses and inhibit viral machinery could be beneficial in effectively controlling the global pandemic.

Non-competitive interactions between hydroxychloroquine and azithromycin: Systematic density functional, molecular dynamics, and docking calculations

In this study, density functional theory (DFT) and docking calculations were systematically performed to study the non-competitive interaction between Hydroxychloroquine (HCQ) and azithromycin (AZTH). The calculated changes in Gibbs free energy and enthalpy (at 310 K) were positive, indicating the non-spontaneous formation of HCQ-AZTH specifically in water media. Docking calculation confirmed the obtained DFT result as evident from the different binding sites of both drugs to the SARS-CoV-2 main protease and human angiotensin-converting enzyme 2 (ACE2) proteins. The HCQ-AZTH structure revealed enhanced electrochemical properties, suggesting the synergy between HCQ and AZTH without affecting their therapeutic efficacy against SARS-CoV-2.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Identification of Vitamin K3 and its analogues as covalent inhibitors of SARS-CoV-2 3CLpro

After the emergence of the pandemic, repurposed drugs have been considered as a quicker way of finding potential antiviral agents. SARS-CoV-2 3CLpro is essential for processing the viral polyproteins into mature non-structural proteins, making it an attractive target for developing antiviral agents. Here we show that Vitamin K3 screened from the FDA-Approved Drug Library containing an array of 1,018 compounds has potent inhibitory activity against SARS-CoV-2 3CLpro with the IC50 value of 4.78 ± 1.03 μM, rather than Vitamin K1, K2 and K4. Next, the time-dependent inhibitory experiment was carried out to confirm that Vitamin K3 could form the covalent bond with SARS-CoV-2 3CLpro. Then we analyzed the structure-activity relationship of Vitamin K3 analogues and identified 5,8-dihydroxy-1,4-naphthoquinone with 9.8 times higher inhibitory activity than Vitamin K3. Further mass spectrometric analysis and molecular docking study verified the covalent binding between Vitamin K3 or 5,8-dihydroxy-1,4-naphthoquinone and SARS-CoV-2 3CLpro. Thus, our findings provide valuable information for further optimization and design of novel inhibitors based on Vitamin K3 and its analogues, which may have the potential to fight against SARS-CoV-2.

Flavonoids in Ampelopsis grossedentata as covalent inhibitors of SARS-CoV-2 3CLpro: Inhibition potentials, covalent binding sites and inhibitory mechanisms

Coronavirus 3C-like protease (3CL^pro) is a crucial target for treating coronavirus diseases including COVID-19. Our preliminary screening showed that Ampelopsis grossedentata extract (AGE) displayed potent SARS-CoV-2-3CL^pro inhibitory activity, but the key constituents with SARS-CoV-2-3CL^pro inhibitory effect and their mechanisms were unrevealed. Herein, a practical strategy via integrating bioactivity-guided fractionation and purification, mass spectrometry-based peptide profiling and time-dependent biochemical assay, was applied to identify the crucial constituents in AGE and to uncover their inhibitory mechanisms. The results demonstrated that the flavonoid-rich fractions (10-17.5 min) displayed strong SARS-CoV-2-3CL^pro inhibitory activities, while the constituents in these fractions were isolated and their SARS-CoV-2-3CL^pro inhibitory activities were investigated. Among all isolated flavonoids, dihydromyricetin, isodihydromyricetin and myricetin strongly inhibited SARS-CoV-2 3CL^pro in a time-dependent manner. Further investigations demonstrated that myricetin could covalently bind on SARS-CoV-2 3CL^pro at Cys300 and Cys44, while dihydromyricetin and isodihydromyricetin covalently bound at Cys300. Covalent docking coupling with molecular dynamics simulations showed the detailed interactions between the orthoquinone form of myricetin and two covalent binding sites (surrounding Cys300 and Cys44) of SARS-CoV-2 3CL^pro. Collectively, the flavonoids in AGE strongly and time-dependently inhibit SARS-CoV-2 3CL^pro, while the newly identified SARS-CoV-2 3CL^pro inhibitors in AGE offer promising lead compounds for developing novel antiviral agents.

Interaction of 8-anilinonaphthalene-1-sulfonate with SARS-CoV-2 main protease and its application as a fluorescent probe for inhibitor identification

The 3C-like main protease of SARS-CoV-2 (3CLPro) is responsible for the cleavage of the viral polyprotein. This process is essential for the viral life cycle. Therefore, 3CLPro is a promising target to develop antiviral drugs for COVID-19 prevention and treatment. Traditional enzymatic assays for the identification of 3CLPro inhibitors rely on peptide-based colorimetric or fluorogenic substrates. However, the COVID-19 pandemic has limit or delay access to these substrates, especially for researchers in developing countries attempting to screen natural product libraries. We explored the use of the fluorescent probe 8-anilinonaphthalene-1-sulfonate (ANS) as an alternative assay for inhibitor identification. Fluorescence enhancement upon binding of ANS to 3CLPro was observed, and this interaction was competitive with a peptide substrate. The utility of ANS-based competitive binding assay to identify 3CLPro inhibitors was demonstrated with the flavonoid natural products baicalein and rutin. The molecular nature of ANS and rutin interaction with 3CLPro was explored with molecular modeling. Our results suggested that ANS could be employed in a competitive binding assay to facilitate the identification of novel SARS-CoV-2 antiviral compounds.

Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants

COVID-19 represents a new potentially life-threatening illness caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pathogen. In 2021, new variants of the virus with multiple key mutations have emerged, such as B.1.1.7, B.1.351, P.1 and B.1.617, and are threatening to render available vaccines or potential drugs ineffective. In this regard, we highlight 3CL^pro, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CL^pro could therefore provide trans-variant effectiveness that is supported by structural studies and possesses readily available biological evaluation experiments. With this in mind, we performed a high throughput virtual screening experiment using CmDock and the "In-Stock" chemical library to prepare prioritisation lists of compounds for further studies. We coupled the virtual screening experiment to a machine learning-supported classification and activity regression study to bring maximal enrichment and available structural data on known 3CL^pro inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CL^pro inhibitor, viral cysteine protease or remaining chemical space based on the calculated set of 208 chemical descriptors. Last but not least, we analysed if the current set of 3CL^pro inhibitors could be used in activity prediction and observed that the field of 3CL^pro inhibitors is drastically under-represented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CL^pro inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial "corona focused libraries".