Molecular modeling,docking and ADMET studies towards development of novel Disopyramide analogs for potential inhibition of human voltage gated sodium channel proteins

The sodium “channelopathies” are the first among the ion channel diseases identified and have attracted widespread clinical and scientific interests. Human voltage gated sodium channels are sites of action of several antiarrhythmic drugs, local anesthetics and related antiepileptic drugs. The present study aims to optimize the activity of Disopyramide, by modification in its structures which may improve the drug action by reducing its side effects. Herein, we have selected Human voltage-gated sodium channel protein type 5 as a potent molecular target. Nearly eighty analogs of Disopyramide are designed and optimized. Thirty are selected for energy minimization using Discovery studio and the LigPrep 2.5. Prior to docking, the active sites of all the proteins are identified. The processing, optimization and minimization of all the proteins is done in Protein preparation wizard. The docking study is performed using the GLIDE. Finally top five ranked lead molecules with better dock scores are identified as having strong binding affinity to 2KAV protein than Disopyramide based on XP G scores. These five leads are further docked with other similar voltage gated sodium channel proteins (PDB IDs: 2KBI, 4DCK, 2L53 and 4DJC) and the best scoring analog with each protein is identified. Drug likeliness and comparative bioactivity analysis for all the analogs is done using QikProp 3.4. Results have shown that the top five lead molecules would have the potential to act as better drugs as compared to Disopyramide and would be of interest as promising starting point for designing compounds against various Sodium channelopathies.     

Proposed lead molecules against Hemagglutinin of avian influenza virus (H5N1)

Human infection with avian influenza H5N1 is an emerging infectious disease characterized by respiratory symptoms and a high fatality rate. Hemagglutinin and neuraminidase are the two surface proteins responsible for infection by influenza virus. Till date, neuraminidase has been the major target for antiviral drugs. In the present study we chose hemagglutinin protein as it mediates the binding of the virus to target cells through sialic acid residues on the host cell-surface. Hemagglutinin of H5 avian influenza (PDB ID: 1JSN) was used as the receptor protein. Ligands were generated by structure-based de novo approach and virtual screening of ZINC database. A total of 11,104 conformers were generated and docked into the receptor binding site using 'High Throughput Virtual Screening'. We proposed potential lead molecules against the receptor binding site of hemagglutinin based on the results obtained from in silico docking and hydrogen bond interaction between the ligand and the 1JSN protein molecule. We found sialic acid derivative 1 to be the lead molecules amongst the ligands generated by structure based de novo approach. However the molecules obtained from ZINC database were showing better docking scores as well as conserved hydrogen bond interactions. Thus we proposed ZINC00487720 and ZINC00046810 as potential lead molecules that could be used as an inhibitor to the receptor binding site of hemagglutinin. They could now be studied in vivo to validate the in silico results.     

Computer aided screening of Accacia nilotica phytochemicals against HCV NS3/4a

Background: HCV has become a leading cause of liver cirrhosis and hepatocellular carcinoma and is a major health concern worldwide. To date, there is no vaccine available in the market to tackle this disease, therefore there is a strong need to develop antiviral compounds that can target all genotypes of HCV with the same efficiency. Medicinal plants have low cost and are less toxic therefore, extracts of medicinal plants can serve as important antiviral agents against HCV. This study was designed to screen phytochemicals of Accacia nilotica to find a potent drug candidate that can inhibit HCV infection effectively.Results: Docking of NS3/4A protease and Flavonoids of Accacia nilotica revealed that most of the flavonoids bound deeply with the active site of NS3/4A protease. Compound 01 showed a high ranking on docking score. All other compounds also showed reliable docking scores and had interactions with the binding cavity of NS3/4A protease, suggesting them as a potent drug candidate to block HCV replication.Conclusion: To recognize binding interactions of Accacia nilotica phytochemicals with NS3/4A protease, molecular docking was performed to find potential inhibitor against NS3/4A protease of HCV. After post docking analysis, important interactions were found between active compounds and active site of NS3/4A protease. It can be concluded from the study that phytochemicals of Accacia nilotica may serve as a potential drug candidate with relatively simple structural changes against HCV NS3/4A protease.     

In silico molecular docking analysis for repurposing approved antiviral drugs against SARS-CoV-2 main protease

Developing a safe and effective antiviral treatment takes a decade, however, when it comes to the coronavirus disease (COVID-19), time is a sensitive matter to slow the spread of the pandemic. Screening approved antiviral drugs against COVID-19 would speed the process of finding therapeutic treatment. The current study examines commercially approved drugs to repurpose them against COVID-19 virus main protease using structure-based in-silico screening. The main protease of the coronavirus is essential in the viral replication and is involved in polyprotein cleavage and immune regulation, making it an effective target when developing the treatment. A Number of approved antiviral drugs were tested against COVID-19 virus using molecular docking analysis by calculating the free natural affinity of the binding ligand to the active site pocket and the catalytic residues without forcing the docking of the ligand to active site. COVID-19 virus protease solved structure (PDB ID: 6LU7) is targeted by repurposed drugs. The molecular docking analysis results have shown that the binding of Remdesivir and Mycophenolic acid acyl glucuronide with the protein drug target has optimal binding features supporting that Remdesivir and Mycophenolic acid acyl glucuronide can be used as potential anti-viral treatment against COVID-19 disease.     

Challenges and advances in computational docking: 2009 in review

Docking is a computational technique that places a small molecule (ligand) in the binding site of its macromolecular target (receptor) and estimates its binding affinity. This review addresses methodological developments that have occurred in the docking field in 2009, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. These developments aim to address the main challenges of docking: receptor representation (such aspects as structural waters, side chain protonation, and, most of all, flexibility (from side chain rotation to domain movement)), ligand representation (protonation, tautomerism and stereoisomerism, and the effect of input conformation), as well as accounting for solvation and entropy of binding. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design.     

A comprehensive molecular interaction map for Hepatitis B virus and drug designing of a novel inhibitor for Hepatitis B X protein

Hepatitis B virus (HBV) infection is a leading source of liver diseases such as hepatitis, cirrhosis and hepatocellular carcinoma. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Hepatitis B virus (HBV). Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by HBV. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. We find that some nodes in the network that prove to be topologically important, in particular HBx is also known to be important target protein used for the treatment of HBV. Therefore, HBx protein is the preferential choice for inhibition to stop the proteolytic processing. Hence, the 3D structure of HBx protein was downloaded from PDB. Ligands for the active site were designed using LIGBUILDER. The HBx protein's active site was explored to find out the critical interactions pattern for inhibitor binding using molecular docking methodology using AUTODOCK Vina. It should be noted that these predicted data should be validated using suitable assays for further consideration.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

The interprotein scoring noises in glide docking scores

Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.     

Progress in molecular docking

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.     

Identification of Potential Inhibitors of H5N1 Influenza A Virus Neuraminidase by Ligand-Based Virtual Screening Approach

The neuraminidase (NA) of the influenza virus is the target of antiviral drug, oseltamivir. Recently, cases were reported that influenza virus becoming resistant to oseltamivir, necessitating the development of new long-acting antiviral compounds. In this report, a novel class of lead molecule with potential NA inhibitory activity was identified using a combination of virtual screening (VS), molecular docking, and molecular dynamic approach. The PubChem database was used to perform the VS analysis by employing oseltamivir as query. Subsequently, the data reduction was carried out by employing molecular docking study. Furthermore, the screened lead molecules were analyzed with respect to the Lipinski rule of five, drug-likeness, toxicity profiles, and other physico-chemical properties of drugs by suitable software program. Final screening was carried out by normal mode analysis and molecular dynamic simulation approach. The result indicates that CID 25145634, deuterium-enriched oseltamivir, become a promising lead compound and be effective in treating oseltamivir sensitive as well as resistant influenza virus strains.     

An efficient computational method for calculating ligand binding affinities

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol⁻¹ of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.     

Secondary metabolites extracted from marine sponge associated Comamonas testosteroni and Citrobacter freundii as potential antimicrobials against MDR pathogens and hypothetical leads for VP40 matrix protein of Ebola virus: an in vitro and in silico investigation

The current study explores therapeutic potential of metabolites extracted from marine sponge (Cliona sp.)-associated bacteria against MDR pathogens and predicts the binding prospective of probable lead molecules against VP40 target of Ebola virus. The metabolite-producing bacteria were characterized by agar overlay assay and as per the protocols in Bergey’s manual of determinative bacteriology. The antibacterial activities of extracted metabolites were tested against clinical pathogens by well-diffusion assay. The selected metabolite producers were characterized by 16S rDNA sequencing. Chemical screening and Fourier Transform Infrared (FTIR) analysis for selected compounds were performed. The probable lead molecules present in the metabolites were hypothesized based on proximate analysis, FTIR data, and literature survey. The drug-like properties and binding potential of lead molecules against VP40 target of Ebola virus were hypothesized by computational virtual screening and molecular docking. The current study demonstrated that clear zones around bacterial colonies in agar overlay assay. Antibiotic sensitivity profiling demonstrated that the clinical isolates were multi-drug resistant, however; most of them showed sensitivity to secondary metabolites (MIC-15 μl/well). The proximate and FTIR analysis suggested that probable metabolites belonged to alkaloids with O–H, C–H, C=O, and N–H groups. 16S rDNA characterization of selected metabolite producers demonstrated that 96% and 99% sequence identity to Comamonas testosteroni and Citrobacter freundii, respectively. The docking studies suggested that molecules such as Gymnastatin, Sorbicillactone, Marizomib, and Daryamide can designed as probable lead candidates against VP40 target of Ebola virus.     

A free energy based computational pathway from chemical templates to lead compounds: a case study of COX-2 inhibitors

Automation of lead compound design in silico given the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz. sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.     

Docking Studies of Pakistani HCV NS3 Helicase: A Possible Antiviral Drug Target

The nonstructural protein 3 (NS3) of hepatitis C virus (HCV) helicase is believed to be essential for viral replication and has become an attractive target for the development of antiviral drugs. The study of helicase is useful for elucidating its involvement in positive sense single-stranded RNA virus replication and to serve as templates for the design of novel antiviral drugs. In recent years, several models have been proposed on the conformational change leading to protein movement and RNA unwinding. Some compounds have been recently reported to inhibit the helicase and these include small molecules, RNA aptamers and antibodies. The current study is designed to help gain insights for the consideration of potential inhibitors for Pakistani HCV NS3 helicase protein. We have cloned, expressed and purified HCV NS3 helicase from Pakistani HCV serum samples and determined its 3D structure and employed it further in computational docking analysis to identify inhibitors against HCV genotype 3a (GT3a),including six antiviral key molecules such as quercetin, beta-carotene, resveratrol, catechins, lycopene and lutein. The conformation obtained after docking showed good hydrogen bond (HBond) interactions with best docking energy for quercetin and catechins followed by resveratrol and lutein. These anti-helicase key molecules will offer an alternative attraction to target the viral helicase, due to the current limitation with the interferon resistance treatment and presences of high rate of resistance in anti-protease inhibitor classes.     

A Free Energy Based Computational Pathway from Chemical Templates to Lead Compounds: A Case Study of COX-2 Inhibitors

Abstract

Automation of lead compound design in silico given the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz. sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.     

Conformer and pharmacophore based identification of peptidomimetic inhibitors of chikungunya virus nsP2 protease

Chikungunya virus nsP2 replication protein is a cysteine protease, which cleaves the nonstructural nsP1234 polyprotein into functional replication components. The cleavage and processing of nsP1234 by nsP2 protease is essential for the replication and proliferation of the virus. Thus, ChikV nsP2 protease is a promising target for antiviral drug discovery. In this study, the crystal structure of the C-terminal domain of ChikV nsP2 protease (PDB ID: 4ZTB) was used for structure based identification and rational designing of peptidomimetic inhibitors against nsP2 protease. The interactions of the junction residues of nsP3/4 polyprotein in the active site of nsP2 protease have been mimicked to identify and design potential inhibitory molecules. Molecular docking of the nsP3/4 junction peptide in the active site of ChikV nsP2 protease provided the structural insight of the probable binding mode of nsP3/4 peptide and pigeonholed the molecular interactions critical for the substrate binding. Further, the shape and pharmacophoric properties of the viral nsP3/4 substrate peptide were taken into consideration and the mimetic molecules were identified and designed. The designed mimetic compounds were then analyzed by docking and their binding affinity was assessed by molecular dynamics simulations.     

Targeting the NTPase site of Zika virus NS3 helicase for inhibitor discovery

Abstract

The major threats linked to Zika virus (ZIKV) are microcephaly, Guillain-Barre syndrome, and the ability to transfer through sexual transmission. Despite these threats, Zika specific FDA approved drugs or vaccines are not available as of yet. Additionally, the involvement of pregnant women makes the drug screening process lengthy and complicated in terms of safety and minimum toxicity of the molecules. Since NS3 helicase of ZIKV performs the critical function of unwinding double-stranded RNA during replication, it is considered as a promising drug target to block ZIKV replication. In the present study, we have exploited the NTPase site of ZIKV NS3 helicase for screening potential inhibitor compounds by molecular docking, and molecular dynamics (MD) simulation approaches. NS3 helicase hydrolyzes the ATP to use its energy for unwinding RNA. We have chosen twenty natural compounds from ZINC library with known antiviral properties and a helicase focused library (HFL) of small molecules from Life Chemicals compounds. After going through docking, the top hit molecules from ZINC and HFL library were further analysed by MD simulations to find out stable binding poses. Finally, we have reported the molecules with potential of binding at NTPase pocket of ZIKV NS3 helicase, which could be further tested on virus through in vitro experiments to check their efficacy.

Communicated by Ramaswamy H. Sarma