Screening of Severe Acute Respiratory Syndrome Coronavirus 2 RNA-Dependent RNA Polymerase Inhibitors Using Computational Approach

Potential inhibitors of FemC to combat Staphylococcus aureus: virtual screening, molecular docking, dynamics simulation, and MM-PBSA analysis

FemC is a methicillin resistance factor involved in the alterations of peptidoglycan and glutamine synthesis in Staphylococcus aureus. To identify the potent antibacterial agents, antibacterial molecules were screened against the predicted and validated FemC model. Based on docking scores, presence of essential interactions with active site residues of FemC, pharmacokinetic, and ADMET properties, six candidates were shortlisted and subjected to molecular dynamics to evaluate the stability of FemC-ligand complexes. Further, per residue decomposition analysis and Molecular Mechanics/Poisson-Boltzmann Surface Area (MMPBSA) analysis confirmed that S15, M16, S17, R31, R43, Q47, K48 and R49 of FemC played a vital role in the formation of lower energy stable FemC-inhibitor(s) complexes. Therefore, in the present study, the reported six molecules (Z317461228, Z92241701, Z30923155, Z30202349, Z2609517102 and Z92470167) may pave the path to design the scaffold of novel potent antimicrobials against S. aureus.Communicated by Ramaswamy H. Sarma.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

A Computational Workflow to Predict Biological Target Mutations: The Spike Glycoprotein Case Study

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Promising antibacterials for LLM of Staphylococcus aureus using virtual screening, molecular docking, dynamics, and MMPBSA

In S. aureus, lipophilic membrane (LLM) protein is a methicillin resistance factor and is an essential role in peptidoglycan metabolism. The virtual screening of antibacterial molecules against the model of LLM was performed to identify the potent antibacterial molecules. Molecular docking results of pharmacokinetic filtered molecules illustrated that five molecules had higher binding affinities than tunicamycin (TUM) and were stabled via non-covalent interactions (hydrogen bond and hydrophobic interactions) at the active site of LLM. Further, molecular dynamics results revealed that binding of identified antibacterial molecules with LLM resulted in stable LLM-inhibitor(s) complexes. Molecular Mechanics/Position-Boltzmann Surface Area (MMPBSA) analysis showed that LLM-inhibitor(s) complexes had high binding affinities in the range of -213.49 ± 2.24 to -227.42 ± 3.05 kJ/mol. The amino acid residues decomposition analysis confirmed that identified antibacterial molecules bound at the active site (Asn148, Leu149, Asp151, Asp208, His269, His271, and His272) of LLM. Noticeably, the current study found five antibacterial molecules (BDE 27575101, BDE 33638168, BDE 33672484, LAS 51502073, and BDE 25098678) were highly potent than TUM and even than earlier reported molecules. Therefore, here reported antibacterial molecules may be used directly or developed to inhibit LLM of S. aureus.Communicated by Ramaswamy H. Sarma.

Deep Drug Discovery of Mac Domain of SARS-CoV-2 (WT) Spike Inhibitors: Using Experimental ACE2 Inhibition TR-FRET Assay, Screening, Molecular Dynamic Simulations and Free Energy Calculations

SARS-CoV-2 exploits the homotrimer transmembrane Spike glycoproteins (S protein) during host cell invasion. The Omicron XBB subvariant, delta, and prototype SARS-CoV-2 receptor-binding domain show similar binding strength to hACE2 (human Angiotensin-Converting Enzyme 2). Here we utilized multiligand virtual screening to identify small molecule inhibitors for their efficacy against SARS-CoV-2 virus using QPLD, pseudovirus ACE2 Inhibition -Time Resolved Forster/Fluorescence energy transfer (TR-FRET) Assay Screening, and Molecular Dynamics simulations (MDS). Three hundred and fifty thousand compounds were screened against the macrodomain of the nonstructural protein 3 of SARS-CoV-2. Using TR-FRET Assay, we filtered out two of 10 compounds that had no reported activity in in vitro screen against Spike S1: ACE2 binding assay. The percentage inhibition at 30 µM was found to be 79% for "Compound F1877-0839" and 69% for "Compound F0470-0003". This first of its kind study identified "FILLY" pocket in macrodomains. Our 200 ns MDS revealed stable binding poses of both leads. They can be used for further development of preclinical candidates.

Molecular modeling study of natural products as potential bioactive compounds against SARS-CoV-2

CONTEXT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 infection and responsible for millions of victims worldwide, remains a significant threat to public health. Even after the development of vaccines, research interest in the emergence of new variants is still prominent. Currently, the focus is on the search for effective and safe drugs, given the limitations and side effects observed for the synthetic drugs administered so far. In this sense, bioactive natural products that are widely used in the pharmaceutical industry due to their effectiveness and low toxicity have emerged as potential options in the search for safe drugs against COVID-19. Following this line, we screened 10 bioactive compounds derived from cholesterol for molecules capable of interacting with the receptor-binding domain (RBD) of the spike protein from SARS-CoV-2 (SC2Spike), responsible for the virus's invasion of human cells. Rounds of docking followed by molecular dynamics simulations and binding energy calculations enabled the selection of three compounds worth being experimentally evaluated against SARS-CoV-2.

METHODS

The 3D structures of the cholesterol derivatives were prepared and optimized using the Spartan 08 software with the semi-empirical method PM3. They were then exported to the Molegro Virtual Docking (MVD®) software, where they were docked onto the RBD of a 3D structure of the SC2Spike protein that was imported from the Protein Data Bank (PDB). The best poses obtained from MVD® were subjected to rounds of molecular dynamics simulations using the GROMACS software, with the OPLS/AA force field. Frames from the MD simulation trajectories were used to calculate the ligand's free binding energies using the molecular mechanics - Poisson-Boltzmann surface area (MM-PBSA) method. All results were analyzed using the xmgrace and Visual Molecular Dynamics (VMD) software.

Marine drugs as putative inhibitors against non-structural proteins of SARS-CoV-2: an in silico study

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is an unprecedented pandemic, threatening human health worldwide. The need to produce novel small-molecule inhibitors against the ongoing pandemic has resulted in the use of drugs such as chloroquine, azithromycin, dexamethasone, favipiravir, ribavirin, remdesivir and azithromycin. Moreover, the reports of the clinical trials of these drugs proved to produce detrimental effects on patients with side effects like nephrotoxicity, retinopathy, cardiotoxicity and cardiomyopathy. Recognizing the need for effective and non-harmful therapeutic candidates to combat COVID-19, we aimed to develop promising drugs against SARS-COV-2.

DISCUSSION

In the current investigation, high-throughput virtual screening was performed using the Comprehensive Marine Natural Products Database against five non-structural proteins: Nsp3, Nsp5, Nsp12, Nsp13 and Nsp15. Furthermore, standard precision (SP) docking, extra precision (XP) docking, binding free energy calculation and absorption, distribution, metabolism, excretion and toxicity studies were performed using the Schrӧdinger suite. The top-ranked 5 hits obtained by computational studies exhibited to possess a greater binding affinity with the selected non-structural proteins. Amongst the five hits, CMNPD5804, CMNPD20924 and CMNPD1598 hits were utilized to design a novel molecule (D) that has the capability of interacting with all the key residues in the pocket of the selected non-structural proteins. Furthermore, 200 ns of molecular dynamics simulation studies provided insight into the binding modes of D within the catalytic pocket of selected proteins.

CONCLUSION

Hence, it is concluded that compound D could be a promising inhibitor against these non-structural proteins. Nevertheless, there is still a need to conduct in vitro and in vivo studies to support our findings.

Genetic conservation across SARS-CoV-2 non-structural proteins - Insights into possible targets for treatment of future viral outbreaks

The majority of SARS-CoV-2 therapeutic development work has focussed on targeting the spike protein, viral polymerase and proteases. As the pandemic progressed, many studies reported that these proteins are prone to high levels of mutation and can become drug resistant. Thus, it is necessary to not only target other viral proteins such as the non-structural proteins (NSPs) but to also target the most conserved residues of these proteins. In order to understand the level of conservation among these viruses, in this review, we have focussed on the conservation across RNA viruses, conservation across the coronaviruses and then narrowed our focus to conservation of NSPs across coronaviruses. We have also discussed the various treatment options for SARS-CoV-2 infection. A synergistic melding of bioinformatics, computer-aided drug-design and in vitro/vivo studies can feed into better understanding of the virus and therefore help in the development of small molecule inhibitors against the viral proteins.

Computational investigation of potent inhibitors against YsxC: structure-based pharmacophore modeling, molecular docking, molecular dynamics, and binding free energy

In S. aureus, ribosome biogenesis GTP-binding (YsxC), a GTPase interacts with 50S subunit and 30S subunit of ribosome, and β' subunit of RNA polymerase and played an important role in protein synthesis. For the identification of potent lead molecules, we have conducted pharmacophore modeling by consideration of pharmacophore features of GTP among YsxC-GTP complex. Virtual screening and molecular docking results displayed that five pharmacokinetic and ADMET filtered molecules-ZINC000006424138, ZINC000095502032, ZINC000225415132, ZINC000095475800, and ZINC000012990761-had higher binding affinities than GTP with YsxC. All the identified molecules shared similar pharmacophore features of GTP and were stabilized via hydrogen bonds and hydrophobic interactions with YsxC. Molecular dynamics analysis revealed that YsxC-inhibitor(s) complexes were lesser dynamics and higher stable than YsxC-GTP complex. Molecular Mechanics/Poisson-Boltzmann Surface Area (MMPBSA) results confirmed that identified molecules bound at the active site (Arg33, Ser34, Asn35, Val36, Lys38, Ser39, Thr40, Thr54, Ser55, Pro58, Lys60, Thr61, Thr144, Lys145, Ser178, and Ile179) of YsxC and formed the lower energy (-190.32 ± 3.46 to -217.03 ± 2.55 kJ/mol) complexes than YsxC-GTP (-157.16 ± 2.89 kJ/mol) complex. The identified molecules in this study can be further tested and utilized to design novel antimicrobial agents for S. aureus.Communicated by Ramaswamy H. Sarma.

Discovery of the key active compounds in Citri Reticulatae Pericarpium (Citrus reticulata "Chachi") and their therapeutic potential for the treatment of COVID-19 based on comparative metabolomics and network pharmacology

Edible herbal medicines contain macro- and micronutrients and active metabolites that can take part in biochemical processes to help achieve or maintain a state of well-being. Citri Reticulatae Pericarpium (CRP) is an edible and medicinal herb used as a component of the traditional Chinese medicine (TCM) approach to treating COVID-19 in China. However, the material basis and related mechanistic research regarding this herb for the treatment of COVID-19 are still unclear. First, a wide-targeted UPLC-ESI-MS/MS-based comparative metabolomics analysis was conducted to screen for the active metabolites of CRP. Second, network pharmacology was used to uncover the initial linkages among these metabolites, their possible targets, and COVID-19. Each metabolite was then further studied via molecular docking with the identified potential SARS-CoV-2 targets 3CL hydrolase, host cell target angiotensin-converting enzyme II, spike protein, and RNA-dependent RNA polymerase. Finally, the most potential small molecule compound was verified by in vitro and in vivo experiments, and the mechanism of its treatment of COVID-19 was further explored. In total, 399 metabolites were identified and nine upregulated differential metabolites were screened out as potential key active metabolites, among which isorhamnetin have anti-inflammatory activity in vitro validation assays. In addition, the molecular docking results also showed that isorhamnetin had a good binding ability with the key targets of COVID-19. Furthermore, in vivo results showed that isorhamnetin could significantly reduced the lung pathological injury and inflammatory injury by regulating ATK1, EGFR, MAPK8, and MAPK14 to involve in TNF signaling pathway, PI3K-Akt signalling pathway, and T cell receptor signaling pathway. Our results indicated that isorhamnetin, as screened from CRP, may have great potential for use in the treatment of patients with COVID-19. This study has also demonstrated that comparative metabolomics combined with network pharmacology strategy could be used as an effective approach for discovering potential compounds in herbal medicines that are effective against COVID-19.