Finding potent inhibitors against SARS-CoV-2 main protease through virtual screening, ADMET, and molecular dynamics simulation studies

COVID-19 (Coronavirus disease 2019) is a transmissible disease initiated and propagated through a new virus strain SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) since 31st December 2019 in Wuhan city of China and the infection has outspread globally influencing millions of people. Here, an attempt was made to recognize natural phytochemicals from medicinal plants, in order to reutilize them against COVID-19 by the virtue of molecular docking and molecular dynamics (MD) simulation study. Molecular docking study showed six probable inhibitors against SARS-CoV-2 Mpro (Main protease), two from Withania somnifera (Ashwagandha) (Withanoside V [10.32 kcal/mol] and Somniferine [9.62 kcal/mol]), one from Tinospora cordifolia (Giloy) (Tinocordiside [8.10 kcal/mol]) and three from Ocimum sanctum (Tulsi) (Vicenin [8.97 kcal/mol], Isorientin 4′-O-glucoside 2″-O-p-hydroxybenzoagte [8.55 kcal/mol] and Ursolic acid [8.52 kcal/mol]). ADMET profile prediction showed that the best docked phytochemicals from present work were safe and possesses drug-like properties. Further MD simulation study was performed to assess the constancy of docked complexes and found stable. Hence from present study it could be suggested that active phytochemicals from medicinal plants could potentially inhibit Mpro of SARS-CoV-2 and further equip the management strategy against COVID-19-a global contagion. Highlights Holistic approach of Ayurvedic medicinal plants to avenge against COVID-19 pandemic. Active phytoconstituents of Ayurvedic medicinal plants Withania somnifera (Ashwagandha), Tinospora cordifolia (Giloy) and Ocimum sanctum (Tulsi) predicted to significantly hinder main protease (Mpro or 3Clpro) of SARS-CoV-2. Through molecular docking and molecular dynamic simulation study, Withanoside V, Somniferine, Tinocordiside, Vicenin, Ursolic acid and Isorientin 4′-O-glucoside 2″-O-p-hydroxybenzoagte were anticipated to impede the activity of SARS-CoV-2 Mpro. Drug-likeness and ADMET profile prediction of best docked compounds from present study were predicted to be safe, drug-like compounds with no toxicity. Communicated by Ramaswamy H. Sarma

Background and objective: To recognize natural phytochemicals from medicinal plants, in order to reutilize them against COVID-19 by the virtue of molecular dynamics (MD) simulation study and molecular docking study COVID-19 is a transmissible disease that is initiated and propagated through a new virus strain SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2). Since 31st December 2019 in Wuhan city of China and the infection has outspread globally infecting many countries. Methods: Molecular dynamics MD simulation interaction analysis, Salt bridge analysis, Flexibility analysis, Ace-2- rbd complex electrostatic component of binding energy calculation method. Results: Molecular docking studies has shown to be having two inhibitors against SARS-CoV-2 Mpro (Main protease), from Withania somnifera (Ashwagandha) (Withanoside V [10.32 kcal/mol] and Somniferine [9.62 kcal/mol]). Inconsolably, SARS-CoV-2 infection in patients with pre-existing disease conditions (e.g., hypertension and diabetes) can cause severe complications and, as a result, mortality. Conclusion: Hence from the present study it could be suggested that, the active phytochemicals from medicinal plants could potentially inhibit Mpro of SARS-CoV-2 and further equip the management strategy against COVID-19-a global contagion. Active phytoconstituents of Ayurvedic medicinal plants Withania somnifera (Ashwagandha) predicted to significantly hinder main protease (Mpro or 3Clpro) of SARS-CoV-2.Through molecular docking and molecular dynamic simulation study, Withanoside V, Somniferine were observed to impede the activity of SARS-CoV-2 Mpro.

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the MetaCoreTM/MetaDrugTM (https://portal.genego.com) platform's antiviral therapeutic activity binary QSAR model. In the 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 Main Protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docked compounds and following Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivotexil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.

Antiviral evaluation of hydroxyethylamine analogs: Inhibitors of SARS-CoV-2 main protease (3CLpro), a virtual screening and simulation approach

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CLpro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ − 11 and ~ − 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, − 14.06 kcal/mol, and MM-GBSA binding free energy, − 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease.Graphical abstractIdentification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MSSupplementary InformationThe online version contains supplementary material available at 10.1007/s11030-022-10513-6.

Main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-2 the new strain of coronavirus. In this study, we evaluated biologically active compounds present in medicinal plants as potential SARS-CoV-2 Mpro inhibitors, using a molecular docking study with Autodock Vina software. Top seven compounds Afzelin, Phloroglucinol, Myricetin-3-O- rutinosid Tricin 7-neohesperidoside, Silybin, Kaempferol and Silychristin among 50 molecules of natural Origin (Algerian Medicinal plants) were selected which had better and significantly low binding energy as compared to the reference molecule with binding affinities of −9.3, −9.3, −9, −8.9, −8.5, 8.3 and −8.3 kcal mol−1 respectively. Then, we analyzed the ADME properties of the best 7 ligands using the Web server SwissADME. Two of small molecules have been shown to be the ideal candidates for further drug development. Finally, the stability of the both compounds complexed with Mpro was validated through molecular dynamics (MD) simulation, they displayed stable trajectory (RMSD, RMSF) and molecular properties with consistent interaction profile in molecular dynamics simulations, moreover, Silybin could form more stable complex with Mpro than Silychristin. Communicated by Ramaswamy H. Sarma

Background: Human African trypanosomiasis (HAT), caused by Trypanosoma brucei, is a neglected tropical disease with limited treatments, highlighting the pressing need for new drugs. Cell division cycle-2-related kinase 12 (CRK12), a pivotal protein involved in the cell cycle regulation of T. brucei, has emerged as a promising therapeutic target for HAT, yet effective CRK12 inhibitors remain lacking. Methods: An integrated strategy combining computational modeling, virtual screening, molecular dynamics (MD) simulations, and experimental validation was adopted to discover potential inhibitors against CRK12. By using the predicted and refined 3D structure of CRK12 from AlphaFold2 and MD simulation, over 1.5 million compounds were screened based on multiple-scale molecular docking, and 26 compounds were selected for evaluation of biological activity based on anti-T. brucei bioassays. Dose–response curves were generated for the most potent inhibitors, and the interaction mechanism between the top four compounds and CRK12 was explored by MD simulations and MM/GBSA binding free energy analysis. Results: Of the 26 compounds, six compounds demonstrated sub-micromolar to low-micromolar IC50 values (0.85–3.50 µM). The top four hits, F733-0072, F733-0407, L368-0556, and L439-0038, exhibited IC50 values of 1.11, 1.97, 0.85, and 1.66 µM, respectively. Binding free energy and energy decomposition analyses identified ILE335, VAL343, PHE430, ALA433, and LEU482 as hotspot residues for compound binding. Hydrogen bonding analysis demonstrated that these compounds can form stable hydrogen bonds with the hinge residue ALA433, ensuring their stable binding within the active site. Conclusions: This study establishes a robust and cost-effective pipeline for CRK12 inhibitor discovery, identifying several novel inhibitors demonstrating promising anti-HAT activity. The newly discovered scaffolds exhibit structural diversity distinct from known CRK12 inhibitors, providing valuable lead compounds for anti-trypanosomal drug development.

AimsCoronavirus disease 2019 (COVID-19) has appeared in Wuhan, China but the fast transmission has led to its widespread prevalence in various countries, which has made it a global concern. Another concern is the lack of definitive treatment for this disease. The researchers tried different treatment options which are not specific. The current study aims to identify potential small molecule inhibitors against the main protease protein of SARS-CoV-2 by the computational approach. Main methodsIn this study, a virtual screening procedure employing docking of the two different datasets from the ZINC database, including 1615 FDA approved drugs and 4266 world approved drugs were used to identify new potential small molecule inhibitors for the newly released crystal structure of main protease protein of SARS-CoV-2. In the following to validate the docking result, molecular dynamics simulations were applied on selected ligands to identify the behavior and stability of them in the binding pocket of the main protease in 150 nanoseconds (ns). Furthermore, binding energy using the MMPBSA approach was also calculated. Key findingsThe result indicates that simeprevir (Hepatitis C virus NS3/4A protease inhibitor) and pyronaridine (antimalarial agent) could fit well to the binding pocket of the main protease and because of some other beneficial features including broad-spectrum antiviral properties and ADME profile, they might be a promising drug candidate for repurposing to the treatment of COVID-19. SignificanceSimeprevir and pyronaridine were selected by the combination of virtual screening and molecular dynamics simulation approaches as a potential candidate for treatment of COVID-19.

Glycogen synthase kinase-3β (GSK-3β) has emerged as a crucial target due to its substantial contribution in various cellular processes. Dysfunctional GSK-3β activity can lead to ion channel disturbances, sustain abnormal excitability, and contribute to the pathogenesis of epilepsy and other GSK-3β-related disorders. A set of 82 pyrazole analogs was utilized to study its structural features using a three-dimensional quantitative structure-activity relationship (3D-QSAR), virtual screening, molecular docking, and molecular dynamics. The QSAR model, validated using internal and external methods, demonstrated robustness with a high correlation coefficient r2training = 0.99, cross-validation coefficient q2 = 0.79, r2test = 0.69, and r2external = 0.74. The "Average of Actives" in the Activity Atlas model identified 17 molecules as active. Subsequent pharmacophore-based virtual screening of 17 actives yielded 70 compounds, which were selected as the prediction set to determine the potential GSK-3β inhibitors. Docking studies pinpointed compound P66 as the promising lead compound, with a docking score of - 10.555kcal/mol. These findings were further supported by electrostatic potential (ESP), electrostatic complementarity (EC), and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analyses. Furthermore, a 500ns molecular dynamics (MD) simulation confirmed the structural and conformational stability of the lead complex throughout the simulation period. As a result, this study suggests that compound P66 holds the potential to be a potent lead candidate for the inhibition of GSK-3β, offering a novel therapeutic approach for GSK-3β related disorders, including epilepsy.

Objective: SARS-CoV-2 is a pandemic virus characterized by upper respiratory tract infection and can range from mild symptoms to severe complications. In this case, drug repurposing and computer-aided studies have become very important to find emergency solutions. In this study, drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Material and Method: Comprehensive virtual screening and molecular docking studies from 8820 drug molecules or candidates obtained from the DrugBank database were performed on the RNA binding protein, 2'-O-methyltransferase, and endoribonuclease of SARS-CoV-2; and potential drug candidates were determined for each target. Virtual screening studies have been done with High-Throughput Virtual Screening (HTVS), Standard Precision (SP), Extra Precision (XP), and Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Also, information about the clinical findings, transmission, pathogenesis, and treatment of SARS-CoV-2 has been given. Result and Discussion: Drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Potential compound recommendations for each drug target were presented. Information was given about key amino acids where active sites of drug target proteins interact with ligands. This study is expected to be useful in target-based drug development studies on the proteins of SARS-CoV-2.

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.

Here, we systematically investigated how the force fields and the partial charge models for ligands affect the ranking performance of the binding free energies predicted by the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) approaches. A total of 46 small molecules targeted to five different protein receptors were employed to test the following issues: (1) the impact of five AMBER force fields (ff99, ff99SB, ff99SB-ILDN, ff03, and ff12SB) on the performance of MM/GBSA, (2) the influence of the time scale of molecular dynamics (MD) simulations on the performance of MM/GBSA with different force fields, (3) the impact of five AMBER force fields on the performance of MM/PBSA, and (4) the impact of four different charge models (RESP, ESP, AM1-BCC, and Gasteiger) for small molecules on the performance of MM/PBSA or MM/GBSA. Based on our simulation results, the following important conclusions can be obtained: (1) for short time-scale MD simulations (1 ns or less), the ff03 force field gives the best predictions by both MM/GBSA and MM/PBSA; (2) for middle time-scale MD simulations (2-4 ns), MM/GBSA based on the ff99 force field yields the best predictions, while MM/PBSA based on the ff99SB force field does the best; however, longer MD simulations, for example, 5 ns or more, may not be quite necessary; (3) for most cases, MM/PBSA with the Tan's parameters shows better ranking capability than MM/GBSA (GB(OBC1)); (4) the RESP charges show the best performance for both MM/PBSA and MM/GBSA, and the AM1-BCC and ESP charges can also give fairly satisfactory predictions. Our results provide useful guidance for the practical applications of the MM/GBSA and MM/PBSA approaches.

The coronaviridae family has caused the most destruction among all the viral families in modern sciences. It is one of the recently discovered and added members of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has caused the global pandemic and significant destruction worldwide. However, scientists worldwide have developed vaccines, which are being given to humans. The mutated strain of the virus has caused various uncertainties about whether the discovered drug and vaccines affect it. Even after the World Health Organization's approval for the vaccines, their effectiveness and protection ratio are still a major concern. At the community level, to this date, there is no medicine available to cure the patients. In this study, we have screened the vast library from Drug Bank and identified N-(4-Aminobutanoyl)-S-(4-methoxybenzyl)-L-cysteinylglycine (NSL-CG) that can work against two major targets of SARS CoV-2, replication-transcription and RNA dependent polymerase. Further, we have performed the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and molecular dynamics simulation of the compound with both proteins individually, giving us enough evidence that the said drugs can work against the two targets together. Inhibiting the action of any of both proteins may lead to retaining the virus, and having a dual-targeted drug can be an extra precise measure for this process. The NSL-CG is an experimental drug belonging to the peptidomimetics class included in the small group of drugs with a docking score of −9.079 kcal/mol with replication-transcription −7.885 kcal/mol with RNA-dependent polymerase. Hence, through the complete flowed study, the NSL-CG can be further experimentally validated in in-vitro and in-vivo conditions before human utilisation. Communicated by Ramaswamy H. Sarma

Interleukin-4-induced gene 1 (IL4I1) is an L-phenylalanine oxidase. As the primary enzyme responsible for degrading tryptophan, IL4I1 generates indole metabolites and kynurenic acid, which act as crucial endogenous ligands to activate the aryl hydrocarbon receptor (AHR). This activation enhances tumor survivability while suppressing the body’s anti-tumor immune response. Consequently, IL4I1 is now recognized as a promising new target for drug development in the realm of cancer immunomodulation. In this study, we employed a strategy combining AlphaFold2 with molecular dynamics (MD) simulations to model receptor conformations our docking model achieved a regression fit with an R2 coefficient of 0.34, providing a robust framework for structure-based virtual screening aimed at identifying potential IL4I1 inhibitors. We then applied this structure-based virtual screening method to a compound library. After further MD simulation and following Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculation of binding free energy and ADMET analysis, five candidate IL4I1 inhibitors were obtained. This study provides an effective in silico approach for the identification of IL4I1 inhibitors and offers a valuable reference for the virtual screening of inhibitors targeting other proteins without known structures.