Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

Discovering Influenza Virus Neuraminidase Inhibitors via Computational and Experimental Studies

Influenza A and B viruses spread out worldwide, causing several global concerns. Discovering neuraminidase inhibitors to prevent influenza A and B viruses is thus of great interest. In this work, a machine learning model was trained and tested to evaluate the ligand-binding affinity to neuraminidase. The model was then used to predict the binding affinity of compounds from the CHEMBL database, which is a manually curated database of bioactive molecules with drug-like properties. The physical insights into the binding process of ligands to neuraminidase were clarified via molecular docking and molecular dynamics simulations. Experimental investigation on enzymatic activity validated our computational results and suggested that 2 compounds were potential inhibitors of neuraminidase of the influenza A and B viruses.

Drug repurposing of pyrazolotriazine derivatives as potential anti-SARS-CoV-2 agents: in vitro and in silico studies

The search for new molecules targeting SARS-CoV-2 has been a priority since 2020. The continuous evolution of new mutants increases the need for more research in the area. One way to find new leads is to repurpose existing drugs and molecules against the required target. Here, we present the in vitro and in silico screening of ten previously synthesized and reported compounds as anti-COVID 19 agents. The compounds were screened in vitro against VERO-E6 cells to find their Cytotoxic Concentration (CC50) and their Inhibitory Concentration (IC50). Compounds 1, 2, and 5 revealed a promising anti-SARS-CoV-2 of (IC50 = 2.4, 11.2 and 2.8 µM), respectively while compounds 3 and 7 showed moderate activity of (IC50 = 17.8 and 26.1 µM) compared to Chloroquine which showed an IC50 of 24.9 µM. Among tested compounds, 1 showed the highest selectivity (CC50/IC50) of 192.8. Docking, molecular dynamics and ADME studies were done to investigate potential interactions between compounds and SARS-CoV-2 targets as well as to study the possibility of using them as lead compounds.

MedChemExpress compounds prevent neuraminidase N1 via physics- and knowledge-based methods

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of ca. 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase. Atomistic simulations, including molecular docking and molecular dynamics simulations, then confirmed the ligand-binding affinity. Furthermore, we clarified the physical insights into the binding process of ligands to neuraminidase. It was found that five compounds, including micronomicin, didesmethyl cariprazine, argatroban, Kgp-IN-1, and AY 9944, are able to inhibit neuraminidase N1 of the influenza A virus. Ten residues, including Glu119, Asp151, Arg152, Trp179, Gln228, Glu277, Glu278, Arg293, Asn295, and Tyr402, may be very important in controlling the ligand-binding process to N1.

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.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Natural Phosphodiesterase-4 Inhibitors with Potential Anti-Inflammatory Activities from Millettia dielsiana

The results of in silico screening of the 50 isolated compounds from Millettia dielsiana against the target proteins PDE4 (PDE4A, PDE4B, and PDE4D) showed binding affinity ranges from -5.81 to -11.56, -5.27 to -13.01, and -5.80 to -12.12 kcal mol-1, respectively, with median values of -8.83, -8.84, and -8.645 kcal mol-1, respectively. Among these compounds, Millesianin F was identified as the most promising PDE4A inhibitor due to its strongest binding affinity with the target protein PDE4A. (-11.56 kcal mol-1). This was followed by the compound 5,7,4'-trihydroxyisoflavone 7-O-β-d-apiofuranosyl-(1→6)-β-d-glucopyranoside (D50) with the binding affinity value of -11.35 kcal mol-1. For the target protein PDE4B, compound D50 exhibited the strongest binding affinity value of -13.01 kcal mol-1, while showing poorer inhibition ability for PDE4D. The 100 ns MD simulation examination (radius of gyration, Solvent Accessible Surface Area (SASA), Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), and hydrogen bonding) was carried out to examine the overall stability and binding efficiency of the protein-ligand complex between compounds (Millesianin F, Millesianin G, Claclrastin-7-O-β-d-glucopyranoside, 7-hydroxy-4',6 dimethoxyisoflavone-7-O-β-d-apiofuranosyl-(1→6)-β-d-glucopyranoside, 7-hydroxy-4',8-dimethoxyisoflavone 7-O-β-d-apiofuranosyl-(1→6)-β-d-glucopyranoside, Odoratin-7-O-β-d-glucopyranoside, and 5,7,4'-trihydroxyisoflavone 7-O-β-d-apiofuranosyl-(1→6)-β-d-glucopyranoside) and PDE4 (A, B) subtype proteins. Compound D50 has shown strong anti-inflammatory activity, as evidenced by experimental results. It effectively inhibits PDE4B and PDE4D, with IC50 values of 6.56 ± 0.7 µM and 11.74 ± 1.3 µM, respectively. Additionally, it reduces NO production, with an IC50 value of 5.40 ± 0.9 µM. Based on these findings, it is promising and considered a potential novel anti-inflammatory drug for future development.

Insight into Tyrosine-Containing Pharmaceuticals as Potential Inhibitors of SARS-CoV-2 3CLpro and NSP16: Structural Analysis, Docking Studies, Molecular Dynamics Simulations, and Density Functional Theory Investigations

Tyrosine-containing pharmaceuticals’ (TPh) potential to inhibit SARS CoV-2 3-chymotrypsin-like proteases (3CLpro) and nonstructural protein 16 (NSP16) has been explored using docking studies, molecular dynamics simulations, and density functional theory. The TPh with FDA approval showed excellent contact with the active site pockets of 3CLpro and NSP16. Their binding affinity scores ranged from −5.8 to −4.9 kcal/mol and −6.3 to −4.8 for 3CLpro and NSP16, respectively. A 100-ns molecular dynamics simulation confirmed the stability of the carbidopa/NSP16 complex and N-acetyl tyrosine with both target enzymes. Further, the HOMO-LUMO transitions, molecular orbitals, and dipole moments of carbidopa, droxidopa, and N-acetyl tyrosine were computed using density functional theory (DFT). Considering N-acetyl tyrosine and carbidopa’s substantial inhibitory activity, it is recommended to investigate them further in order to explore their application for the treatment of COVID-19 or any other coronaviruses in the future.

Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3–d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert-butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl₂•8H₂O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3-d]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (M^Pro) and papain-like protease (PL^Pro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3-d]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein-ligand-type molecular docking studies on the human body temperature-dependent M^Pro protein that surprisingly contains zinc^II (Zn^II) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 M^Pro inhibitors, is performed for the first time in this paper, to the best of our knowledge.

Computational Analysis of Triazole-Based Kojic Acid Analogs as Tyrosinase Inhibitors by Molecular Dynamics and Free Energy Calculations

Molecular docking, molecular dynamics (MD) simulations and the linear interaction energy (LIE) method were used here to predict binding modes and free energy for a set of 1,2,3-triazole-based KA analogs as potent inhibitors of Tyrosinase (TYR), a key metalloenzyme of the melanogenesis process. Initially, molecular docking calculations satisfactorily predicted the binding mode of evaluated KA analogs, where the KA part overlays the crystal conformation of the KA inhibitor into the catalytic site of TYR. The MD simulations were followed by the LIE method, which reproduced the experimental binding free energies for KA analogs with an r² equal to 0.97, suggesting the robustness of our theoretical model. Moreover, the van der Waals contributions performed by some residues such as Phe197, Pro201, Arg209, Met215 and Val218 are responsible for the binding recognition of 1,2,3-triazole-based KA analogs in TYR catalytic site. Finally, our calculations provide suitable validation of the combination of molecular docking, MD, and LIE approaches as a powerful tool in the structure-based drug design of new and potent TYR inhibitors.

In Silico Study towards Repositioning of FDA-Approved Drug Candidates for Anticoronaviral Therapy: Molecular Docking, Molecular Dynamics and Binding Free Energy Calculations

The SARS-CoV-2 targets were evaluated for a set of FDA-approved drugs using a combination of drug repositioning and rigorous computational modeling methodologies such as molecular docking and molecular dynamics (MD) simulations followed by binding free energy calculations. Six FDA-approved drugs including, Ouabain, Digitoxin, Digoxin, Proscillaridin, Salinomycin and Niclosamide with promising anti-SARS-CoV-2 activity were screened in silico against four SARS-CoV-2 proteins—papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), SARS-CoV-2 main protease (Mpro), and adaptor-associated kinase 1 (AAK1)—in an attempt to define their promising targets. The applied computational techniques suggest that all the tested drugs exhibited excellent binding patterns with higher scores and stable complexes compared to the native protein cocrystallized inhibitors. Ouabain was suggested to act as a dual inhibitor for both PLpro and Mpro enzymes, while Digitoxin bonded perfectly to RdRp. In addition, Salinomycin targeted PLpro. Particularly, Niclosamide was found to target AAK1 with greater affinity compared to the reference drug. Our study provides comprehensive molecular-level insights for identifying or designing novel anti-COVID-19 drugs.

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

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

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC₅₀ values of 0.51 and 0.33 μM, respectively. The obtained IC₅₀ of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic, resulting in several million deaths being reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of the SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 (Nirmatrelvir) is currently under clinical trials for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 μs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. The successfully generated bound state between the two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of the thioimidate product from SARS-CoV-2 Mpro and the PF-07321332 inhibitor. We found that the catalytic triad Cys145-His41-Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for a better understanding of the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters

AutoDock Vina (Vina) achieved a very high docking-success rate, p ^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R set 1 = 0.556 ± 0.025 compared with R Default = 0.493 ± 0.028 obtained by the original Vina and R Vina 1.2 = 0.503 ± 0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R ≥ 0.500 for 32/48 targets, compared with the default package, giving R ≥ 0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( R set 1 = 0.617 ± 0.017 ) than the default package ( R Default = 0.543 ± 0.020 ) and Vina version 1.2 ( R Vina 1.2 = 0.540 ± 0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

Inhibitory activity of FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, remdesivir, and hydroxychloroquine on COVID-19 main protease and human ACE2 receptor: A comparative in silico approach

By September 1, 2021, SARS-CoV-2, a respiratory virus that prompted Coronavirus Disease in 2019, had infected approximately 218,567,442 patients and claimed 4,534,151 lives. There are currently no specific treatments available for this lethal virus, although several drugs, including remdesivir and hydroxychloroquine, have been tested. The purpose of this study is to assess the activity of FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, remdesivir, and hydroxychloroquine as potential SARS-CoV-2 main protease inhibitors. Additionally, this study aims to provide insight into the development of potential inhibitors that may inhibit ACE2, thereby preventing SARS-CoV-2 entry into the host cell and infection. To this end, remdesivir and hydroxychloroquine were used as comparator drugs. The calculations revealed that cetilistat, abiraterone, diiodohydroxyquinoline, and bexarotene inhibit main protease and ACE2 receptors more effectively than the well-known drug hydroxychloroquine when used against COVID-19. Meanwhile, bexarotene and cetilistat bind more tightly to the SARS-CoV-2 main protease and the ACE2 receptor, respectively, than remdesivir, a potential treatment for COVID-19 that is the first FDA-approved drug against this virus. As a result, the molecular dynamic simulations of these two drugs in the presence of proteins were investigated. The MD simulation results demonstrated that these drugs interact to stabilize the systems, allowing them to be used as effective inhibitors of these proteins. Meanwhile, bexarotene, abiraterone, cetilistat, and diiodohydroxyquinoline's systemic effects should be further investigated in suitable ex vivo human organ culture or organoids, animal models, or clinical trials.

Marine derivatives prevent wMUS81 in silico studies

The winged-helix domain of the methyl methanesulfonate and ultraviolet-sensitive 81 (wMUS81) is a potential cancer drug target. In this context, marine fungi compounds were indicated to be able to prevent wMUS81 structure via atomistic simulations. Eight compounds such as D197 (Tryptoquivaline U), D220 (Epiremisporine B), D67 (Aspergiolide A), D153 (Preussomerin G), D547 (12,13-dihydroxyfumitremorgin C), D152 (Preussomerin K), D20 (Marinopyrrole B) and D559 (Fumuquinazoline K) were indicated that they are able to prevent the conformation of wMUS81 via forming a strong binding affinity to the enzyme via perturbation approach. The electrostatic interaction is the dominant factor in the binding process of ligands to wMUS81. The residues Trp55, Arg59, Leu62, His63 and Arg69 were found to frequently form non-bonded contacts and hydrogen bonds to inhibitors. Moreover, the influence of the ligand D197, which formed the lowest binding free energy to wMUS81, on the structural change of enzyme was investigated using replica exchange molecular dynamics simulations. The obtained results indicated that D197, which forms a strong binding affinity, can modify the structure of wMUS81. Overall, the marine compounds probably inhibit wMUS81 due to forming a strong binding affinity to the enzyme as well as altering the enzymic conformation.

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (Mpro) of SARS-CoV-2

The main protease (Mpro or 3CLpro) is a conserved cysteine protease from the coronaviruses and started to be considered an important drug target for developing antivirals, as it produced a deadly outbreak of COVID-19. Herein, we used a combination of drug reposition and computational modeling approaches including molecular docking, molecular dynamics (MD) simulations, and the calculated binding free energy to evaluate a set of drugs in complex with the Mpro enzyme. Particularly, our results show that darunavir and triptorelin drugs have favorable binding free energy (-63.70 and -77.28 kcal mol-1, respectively) in complex with the Mpro enzyme. Based on the results, the structural and energetic features that explain why some drugs can be repositioned to inhibit Mpro from SARS-CoV-2 were exposed. These features should be considered for the design of novel Mpro inhibitors.