Pharmacophore feature prediction and molecular docking approach to identify novel anti-HCV protease inhibitors

Identification of coumarin derivatives targeting acetylcholinesterase for Alzheimer's disease by field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, MM/GBSA, ADME and MD Simulation study

Alzheimer's disease (AD) leads to gradual memory loss including other compromised cognitive abilities. Acetylcholinesterase (AChE), an important biochemical enzyme from the cholinesterase (ChE) family, is recognized as primary pharmacological target for treating AD. Currently marketed drugs for AD treatment are primarily AChE inhibitors and coumarin derivatives comprising a wide variety of pharmacological activities have proved their efficacy towards AChE inhibition. Ensaculin (KA-672 HCl), a compound that belong to the coumarin family, is a clinical trial candidate for AD treatment. Therefore, a ligand library was prepared with 60 reported coumarin derivatives for field-based 3D-QSAR and pharmacophore modelling. The field-based 3D-QSAR model obtained at partial least square (PLS) factor 7, was the best validated model that predicted activity closer to original activity for each ligand introduced. The contour maps demonstrated spatial distribution of favourable and unfavorable steric, hydrophobic, electrostatic and H-bond donor and acceptor contours around coumarin nucleus. The best pharmacophore model, ADHRR_1 exhibited five essential pharmacophoric features of four different traits for optimum AChE inhibition. Virtual screening through ADHRR_1 accompanied with molecular docking and MM/GBSA identified 10 HITs from a 4,00,000 coumarin derivatives from PubChem database. HITs comprised docking scores ranging from -12.096 kcal/mol to -8.271 kcal/mol and compared with the reference drug Donepezil (-8.271 kcal/mol). ADME properties analysis led into detecting two leads (HIT 1 and HIT 2) among these 10 HITs. Molecular Dynamics Simulation indicated thermodynamic stability of the complex of lead compounds with AChE protein. Finally, thorough survey of the experimental results from 3D-QSAR modelling, pharmacophore modelling and molecular docking interactions led us to develop the lead formula I for future advancements in treating AD through AChE inhibitors.

Discovery of novel glucosinolates inhibiting advanced glycation end products: Virtual screening and molecular dynamic simulation

Protein glycation can result in the formation of advanced glycation end products (AGEs), which pose a potential health risk due to their association with diabetic complications. Natural products are a source of drugs discovery and the search for potential natural inhibitors of AGEs is of great significance. Glucosinolates (GSLs) mainly from cruciferous plants have potential antioxidant, anti-inflammatory, and anti-glycation activities. In this study, the inhibitory activity of GSLs on bovine serum albumin (BSA) along with its mechanism was investigated by virtual screening and various computational simulation techniques. Virtual screening revealed that 174 GSLs were screened using Maestro based on the glide score and 89% of the compounds were found to have potential anti-glycation ability with the docking scores less than -5 kcal/mol. Molecular docking showed that the top 10 GSLs were bound to the IIA structural domain of BSA. Among them, glucohesperin (1) and 2-hydroxyethyl glucosinolate (2) had the lowest docking scores of -9.428 and -9.333 kcal/mol, respectively, reflecting their good binding affinity. Molecular dynamics simulations of 1 (ΔG = -43.46 kcal/mol) and 2 (ΔG = -43.71 kcal/mol) revealed that the complexes of these two compounds with proteins had good stability. Further binding site analysis suggested that the mechanism of inhibition of protein glycation by these two active ingredients might be through competitive hydrogen bonding to maintain the structural integrity of the protein, thus inhibiting glycation reaction. Moreover, the ADMET values and CYP450 metabolism prediction data were within the recommended values. Therefore, it can be concluded that 1 and 2 may act as potential anti-glycation agents.

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the “Anti-HCV” platform using machine learning and quantitative structure–activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC₅₀/EC₅₀) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson’s correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The “Anti-HCV” predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.

In Silico design of AVP (4-5) peptide and synthesis, characterization and in vitro activity of chitosan nanoparticles

BACKGROUND

Arginine-vasopressin (AVP) is a neuropeptide and provides learning and memory modulation. The AVP (4-5) dipeptide corresponds to the N-terminal fragment of the major vasopressin metabolite AVP (4-9), has a neuroprotective effect and used in the treatment of Alzheimer's and Parkinson's disease.

METHODS

The main objective of the present study is to evaluate the molecular mechanism of AVP (4-5) dipeptide and to develop and synthesize chitosan nanoparticle formulation using modified version of ionic gelation method, to increase drug effectiveness. For peptide loaded chitosan nanoparticles, the synthesized experiment medium was simulated for the first time by molecular dynamics method and used to determine the stability of the peptide, and the binding mechanism to protein (HSP70) was also investigated by molecular docking calculations. A potential pharmacologically features of the peptide was also characterized by ADME (Absorption, Distribution, Metabolism and Excretion) analysis. The characterization, in vitro release study, encapsulation efficiency and loading capacity of the peptide loaded chitosan nanoparticles (CS NPs) were performed by Dynamic Light Scattering (DLS), UV-vis absorption (UV), Scanning Electron Microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy techniques. Additionally, in vitro cytotoxicity of the peptide on human neuroblastoma cells (SH-SY5Y) was examined with XTT assay and the statistical analysis was evaluated.

RESULTS

The results showed that; hydrodynamic size, zeta potential and polydispersity index (PdI) of the peptide-loaded CS NPs were 167.6 nm, +13.2 mV, and 0.211, respectively. In vitro release study of the peptide-loaded CS NPs showed that 17.23% of the AVP (4-5)-NH2 peptide was released in the first day, while 61.13% of AVP (4-5)-NH2 peptide was released in the end of the 10th day. The encapsulation efficiency and loading capacity were 99% and 10%, respectively. According to the obtained results from XTT assay, toxicity on SHSY-5Y cells in the concentration from 0.01 μg/μL to 30 μg/μL were evaluated and no toxicity was observed. Also, neuroprotective effect was showed against H₂O₂ treatment.

CONCLUSION

The experimental medium of peptide-loaded chitosan nanoparticles was created for the first time with in silico system and the stability of the peptide in this medium was carried out by molecular dynamics studies. The binding sites of the peptide with the HSP70 protein were determined by molecular docking analysis. The size and morphology of the prepared NPs capable of crossing the blood-brain barrier (BBB) were monitored using DLS and SEM analyses, and the encapsulation efficiency and loading capacity were successfully performed with UV Analysis. In vitro release studies and in vitro cytotoxicity analysis on SHSY-5Y cell lines of the peptide were conducted for the first time. Grapical abstract.

Computational Drug Design against Ebola Virus Targeting Viral Matrix Protein VP30

Ebola viral disease (EVD) is a deadly infectious hemorrhagic viral fever caused by the Ebola virus with a high mortality rate. Until date, there is no effective drug or vaccination available to combat this condition. This study focuses on designing an effective antiviral drug for Ebola viral disease targeting viral protein 30 (VP30) of Ebola virus, highly required for transcription initiation. The lead molecules were screened for Lipinski rule of five, ADMET study following which molecular docking and bioactivity prediction was carried out. The compounds with the least binding energy were analyzed using interaction software. The results revealed that 6-Hydroxyluteolin and (-)-Arctigenin represent active lead compounds that inhibit the activity of VP30 protein and exhibits efficient pharmacokinetics. Both these compounds are plant-derived flavonoids and possess no known adverse effects on human health. In addition, they bind strongly to the predicted binding site centered on Lys180, suggesting that these two lead molecules can be imperative in designing a potential drug for EVD.

Virtual screening of National Cancer Institute database for claudin-4 inhibitors: Synthesis, biological evaluation, and molecular dynamics studies

Claudin-4 (CLDN4) is a vital member of tight-junction proteins that is often overexpressed in cancer and other malignancies. The three-dimensional structure of human CLDN4 was constructed based on homology modeling approach. A total of 265 242 molecules from the National Cancer Institute (NCI) database has been utilized as a dataset for this study. In the present work, structure-based virtual screening is performed with the NCI database using Glide. By molecular docking, 10 candidate molecules with high scoring functions, which binds to the active site of CLDN4 were identified. Subsequently, molecular dynamics simulations of membrane protein were used for optimization of the top-three lead compounds (NCI110039, NCI344682, and NCI661251) with CLDN4 in a dynamic system. The lead molecule from NCI database NCI11039 (purpurogallin carboxylic acid) was synthesized and cytotoxic properties were evaluated with A549, MCF7 cell lines. Our docking and dynamics simulations predicted that ARG31, ASN142, ASP146, and ARG158 as critically important residues involved in the CLDN4 activity. Finally, three lead candidates from the NCI database were identified as potent CLDN4 inhibitors. Cytotoxicity assays had proved that purpurogallin carboxylic acid had an inhibitory effect towards breast (MCF7) and lung (A549) cancer cell lines. Computational insights and in vitro (cytotoxicity) studies reported in this study are expected to be helpful for the development of novel anticancer agents.

Review on chemogenomic approaches towards hepatitis C viral targets

Hepatitis C virus (HCV) is the most prevalent viral pathogen that infects more than 185 million people worldwide. HCV infection leads to chronic liver diseases such as liver cirrhosis and hepatocellular carcinoma. Direct-acting antivirals (DAAs) are the recent combination therapy for HCV infection with reduced side effects than prior therapies. Sustained virological response (SVR) acts as a gold standard marker to monitor the success of antiviral treatment. Older treatment therapies attain 50-55% of SVR compared with DAAs which attain around 90-95%. The current review emphasizes the recent chemogenomic updates that have been unfolded through structure-based drug design of HCV drug target proteins (NS3/4A, NS5A, and NS5B) and ligand-based drug design of DAAs in achieving a stable HCV viral treatment strategies.

A comparative study of the efficiency of HCV NS3/4A protease drugs against different HCV genotypes using in silico approaches

AIMS

To investigate the efficacy of Direct Acting Antivirals (DAAs) in the treatment of different Hepatitis C Virus (HCV) genotypes.

MAIN METHODS

Homology modeling is used to predict the 3D structures of different genotypes while molecular docking is employed to predict genotype - drug interactions (Binding Mode) and binding free energy (Docking Score).

KEY FINDINGS

Simeprevir (TMC435) and to a lesser degree MK6325 are the best drugs among the studied drugs. The predicted affinity of drugs against genotype 1a is always better than other genotypes. P2-P4 macrocyclic drugs show better performance against genotypes 2, 3 and 5. Macrocyclic drugs are better than linear drugs.

SIGNIFICANCE

HCV is one of the major health problems worldwide. Until the discovery of DAAs, HCV treatment faced many failures. DAAs target key functional machines of the virus life cycle and shut it down. NS3/4A protease is an important target and several drugs have been designed to inhibit its functions. There are several NS3/4A protease drugs approved by Food and Drug Administration (FDA). Unfortunately, the virus exhibits resistance against these drugs. This study is significant in elucidating that no one drug is able to treat different genotypes with the same efficiency. Therefore, treatment should be prescribed based on the HCV genotype.