Promising antivirals for PLpro of SARS-CoV-2 using virtual screening, molecular docking, dynamics, and MMPBSA

Novel nitric oxide donors are coronary vasodilators that also bind to the papain-like protease of SARS-CoV-2

Several investigational nitric oxide donors were originally created to correct vascular endothelial dysfunction in cardiovascular diseases. These 48 compounds contain an urea-like moiety attached to the well-known NO donors isosorbide 2- and 5-mononitrate. CR-0305 and CR-0202 were synthesized and found to be nontoxic in the cell lines HMEC-1, A549/hACE2 and VeroE6. CR-0305 induced vasodilation in human coronary arteries ex vivo. Since NO can also have antiviral properties, a study of drug-protein interactions with SARS-CoV-2 was undertaken using in silico modeling. CR-0305 experimentally outperformed the other compounds, including CR-0202, in binding the catalytic site of SARS-CoV-2 papain-like protease (PLpro). PLpro is a primary target for therapeutic inhibition of SARS-CoV-2 as it mediates viral replication and modulates host innate immune responses. CR-0305 is predicted to sit firmly in the PLpro catalytic pocket as confirmed by molecular dynamics simulations, wherein stability of binding to the catalytic site of PLpro induces a conformational change in the BL2 loop to a more closed conformation as observed previously with GRL0617. Surface plasmon resonance was performed with CR-0305 and CR-0202 to characterize binding affinity to purified SARS-CoV-2 PLpro protein. CR-0305 and CR-0202 also inhibited SARS-CoV-2 infection compared to vehicle as measured by virus N protein staining with a specific antibody in A549-ACE2 and VeroE6 cells at 20 µM. CR-0305 is a coronary vasodilator that appears to bind to the catalytic site of the PLpro of SARS-CoV-2 while targeting delivery of antiviral NO to cells infected by SARS-CoV-2, suggesting multiple indications for future development.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes

Introduction

Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease.

Methods

In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability.

Results

Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type.

Discussion

Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data

Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike protein-mediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.

Structural and energetic analysis of NS5 protein inhibition by small molecules in Japanese encephalitis virus using machine learning and steered molecular dynamics approach

One of the viral diseases that affect millions of people around the world, particularly in developing countries, is Japanese encephalitis (JE). In this study, the conserved protein of this virus, that is, non-structural protein 5 (NS5), was used as a target protein for this study, and a compound library of 749 antiviral molecules was screened against NS5. The current study employed machine learning-based virtual screening combined with molecular docking. Here, three hits (24360, 123519051 and 213039) had lower binding energies (< -8 kcal/mol) than the control, S-Adenosyl-L-homocysteine (SAH). All the compounds showed significant H-bond interactions with functional residues, which were also observed by the control. Molecular dynamics simulation, MM/GBSA for binding free energy analysis, principal component analysis and free energy landscape were also performed to study the stability of the complex formation. All three compounds had similar root mean square deviation trends, which were comparable to the control, SAH. Post-MD, the 123519051-receptor complex had the highest number of H-bonds (4 to 5) after the control, out of which three exhibited the highest percentage occupancy (50%, 24% and 79%). Both docking and MD, 123519051 showed an H-bond with the residue Gly111, which was also found for the control-protein complex. 123519051 showed the lowest binding free energy with ΔGbind of -89 kJ/mol. Steered molecular dynamics depicted that 123519051 had the maximum magnitude of dissociation (1436.43 kJ/mol/nm), which was more than the control, validating its stable complex formation. This study concluded that 123519051 is a binder and could inhibit the protein NS5 of JE.Communicated by Ramaswamy H. Sarma.

Molecular Modelling of Resveratrol Derivatives with SIRT1 for the Stimulation of Deacetylase Activity

BACKGROUND

Resveratrol is a polyphenol that is found in plants and has been proposed to have a potential therapeutic effect through the activation of SIRT1, which is a crucial member of the mammalian NAD+ -dependent deacetylases. However, how its activity is enhanced toward specific substrates by resveratrol derivatives has not been studied. This study aimed to evaluate the types of interaction of resveratrol and its derivatives with SIRT1 as the target protein, as well as to find out the best ligand with the strangest interaction with SIRT1.

MATERIALS AND METHODS

In this study, we employed the extensive molecular docking analysis using AutoDock Vina to comparatively evaluate the interactions of resveratrol derivatives (22 molecules from the ZINC database) as ligands with SIRT1 (PDB ID: 5BTR) as a receptor. The ChemDraw and Chem3D tools were used to prepare 3D structures of all ligands and energetically minimize them by the MM2 force field.

RESULTS

The molecular docking and visualizations showed that conformational change in resveratrol derivatives significantly influenced the parameter for docking results. Several types of interactions, including conventional hydrogen bonds, carbon-hydrogen bonds, Pi-donor hydrogen bonds, and Pi-Alkyl, were found via docking analysis of resveratrol derivatives and SIRT1 receptors. The possible activation effect of resveratrol 4'-(6-galloylglucoside) with ZINC ID: ZINC230079516 with higher binding energy score (-46.8608 kJ/mol) to the catalytic domain (CD) of SIRT1 was achieved at the maximum value for SIRT1, as compared to resveratrol and its other derivatives.

CONCLUSION

Finally, resveratrol 4'-(6-galloylglucoside), as a derivative for resveratrol, has stably interacted with the CD of SIRT1 and might be a potential effective activator for SIRT1.

The Potential Roles of Ficus carica Extract in the Management of COVID-19 Viral Infections: A Computer-aided Drug Design Study

INTRODUCTION

The conventional processes of drug discovery are too expensive, timeconsuming and the success rate is limited. Searching for alternatives that have evident safety and potential efficacy could save money, time and improve the current therapeutic regimen outcomes.

METHODS

Clinical phytotherapy implies the use of extracts of natural origin for prophylaxis, treatment, or management of human disorders. In this work, the potential role of common Fig (Ficus carica) in the management of COVID-19 infections has been explored. The antiviral effects of Cyanidin 3-rhamnoglucoside which is abundant in common Figs have been illustrated on COVID-19 targets. The immunomodulatory effect and the ability to ameliorate the cytokine storm associated with coronavirus infections have also been highlighted. This work involves various computational studies to investigate the potential roles of common figs in the management of COVID-19 viral infections.

RESULTS

Two molecular docking studies of all active ingredients in common Figs were conducted starting with MOE to provide initial insights, followed by Autodock Vina for further confirmation of the results of the top five compounds with the best docking score.

CONCLUSION

Finally, Molecular dynamic simulation alongside MMPBSA calculations were conducted using GROMACS to endorse and validate the entire work.

In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host's Heat Shock Proteins That May Contribute to Viral Replications and Development

The non-structural protein 2 (NSP2) is an RNA-binding protein involved in coronavirus genome replication, and it often decreases human immune response to promote viral invasion and development. It is believed that the NSP2 associates itself with polyamines and heat shock proteins inside the host cell to proceed with viral development. This study aimed to investigate how the SARS-CoV-2 virus' key non-structural proteins (NSP2) utilize polyamines and heat shock proteins using a molecular docking approach and molecular dynamics (MD). ClusPro and HADDOCK servers were used for the docking and Discovery Studio, chimera, and PyMOL were used for analysis. Docking of the heat shock proteins 40 (HSP40), 70 (HSP70), and 90 (HSP90) with SARS-CoV-2 NSP2 resulted in 32, 28, and 19 interactions, respectively. Molecular dynamics revealed Arg458, Asn508, Met297, Arg301, and Trp417 as active residues, and pharmacophore modeling indicated ZINC395648, ZINC01150525, and ZINC85324008 from the zinc database as possible inhibitors for this NSP2.

Temperature dependent molecular dynamics simulation study to understand the stabilizing effect of NADES on the protein β-Lactoglobulin

The thermal stability of a protein is an important concern for its practical application in food processing industries. In this study, we have carried out classical molecular dynamics simulations to systematically investigate the effect of NADES (natural deep eutectic solvent) on the stabilization of the protein β-Lactoglobulin (BLG) at different temperatures. This study sheds light on the very aspects of NADES composed of betaine and sorbitol on the stability of the protein. NADES provides better stability to the protein up to a temperature of 400 K than in water. It is observed that the protein starts to unfold above temperature 400 K in spite of the presence of NADES which is quiet evident from the root mean square deviation (RMSD) and radius of gyration (Rg) plots. The decreasing average solvent accessible surface area (SASA) values and increasing intra-protein hydrogen bonds indicate better stability of the protein in NADES medium than in water at temperatures 300 K and 400 K. At high temperatures viz. 450 K and 500 K the number and distribution of solvent species (betaine and sorbitol) around the protein surface show an increment that are evident from the calculations of solvation shell, radial and spatial distribution functions. Increased number of betaine molecules that interact with the protein through electrostatic interaction may lead to destabilization of the protein at these temperatures. This study suggests that NADES could be used as an ideal medium for thermal stability of the protein BLG up to a temperature of 400 K. Beyond this temperature, NADES used for this study fails to exert stabilization effect on the protein.

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.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

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.

Protein structural insights into a rare PCSK9 gain-of-function variant (R496W) causing familial hypercholesterolemia in a Saudi family: whole exome sequencing and computational analysis

Familial hypercholesterolemia (FH) is a globally underdiagnosed genetic condition associated with premature cardiovascular death. The genetic etiology data on Arab FH patients is scarce. Therefore, this study aimed to identify the genetic basis of FH in a Saudi family using whole exome sequencing (WES) and multidimensional bioinformatic analysis. Our WES findings revealed a rare heterozygous gain-of-function variant (R496W) in the exon 9 of the PCSK9 gene as a causal factor for FH in this family. This variant was absent in healthy relatives of the proband and 200 healthy normolipidemic controls from Saudi Arabia. Furthermore, this variant has not been previously reported in various regional and global population genomic variant databases. Interestingly, this variant is classified as "likely pathogenic" (PP5) based on the variant interpretation guidelines of the American College of Medical Genetics (ACMG). Computational functional characterization suggested that this variant could destabilize the native PCSK9 protein and alter its secondary and tertiary structural features. In addition, this variant was predicted to negatively influence its ligand-binding ability with LDLR and Alirocumab antibody molecules. This rare PCSK9 (R496W) variant is likely to expand our understanding of the genetic basis of FH in Saudi Arabia. This study also provides computational structural insights into the genotype-protein phenotype relationship of PCSK9 pathogenic variants and contributes to the development of personalized medicine for FH patients in the future.

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.

Combining computational and experimental evidence on the activity of antimalarial drugs on papain-like protease of SARS-CoV-2: A repurposing study

The development of inhibitors that target the papain-like protease (PLpro) has the potential to counteract the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent causing coronavirus disease 2019 (COVID-19). Based on a consideration of its several downstream effects, interfering with PLpro would both revert immune suppression exerted by the virus and inhibit viral replication. By following a repurposing strategy, the current study evaluates the potential of antimalarial drugs as PLpro inhibitors, and thereby the possibility of their use for treatment of SARS-CoV-2 infection. Computational tools were employed for structural analysis, molecular docking, and molecular dynamics simulations to screen antimalarial drugs against PLpro, and in silico data were validated by in vitro experiments. Virtual screening highlighted amodiaquine and methylene blue as the best candidates, and these findings were complemented by the in vitro results that indicated amodiaquine as a μM PLpro deubiquitinase inhibitor. The results of this study demonstrate that the computational workflow adopted here can correctly identify active compounds. Thus, the highlighted antimalarial drugs represent a starting point for the development of new PLpro inhibitors through structural optimization.