Identification of Potential Lead Compounds Targeting Novel Druggable Cavity of SARS-CoV-2 Spike Trimer by Molecular Dynamics Simulations

In Silico Exploration of Isoxazole Derivatives of Usnic Acid: Novel Therapeutic Prospects Against α-Amylase for Diabetes Treatment

Diabetes mellitus (DM) a metabolic disorder characterized by high blood sugar levels causing damage to various organs over time. Current anti-diabetic drugs have limitations and side effects, prompting a search for new inhibitors targeting the α-amylase enzyme. This study aims to discover such inhibitors from thirty isoxazole derivatives of usnic acid using in silico approaches. The potential inhibitory effects of compounds were investigated using ADMET, molecular docking, molecular dynamic simulation, principal component analysis and density functional theory studies. ADMET analysis exhibited a wide range of physicochemical, pharmacokinetic, and drug-like qualities with no significant side effects which were then investigated using molecular docking experiment to determine the lead compound with the best binding affinity for the α-amylase enzyme. All compounds showed good binding affinity against α-amylase enzyme (-7.9 to -9.2 kcal/mol) where compound-13 showed the best binding affinity of -9.2 kcal/mol forming hydrogen bonds with Leu162, Tyr62, Glu233 and Asp300 amino acids. Furthermore, the binding posture and the stability of the compound-13-α-amylase enzyme complex was confirmed by molecular dynamic simulation experiment. Moreover, compound-13 showed binding energy value of -27.92 ± 5.61 kcal/mol, which indicated it could be an α-amylase inhibitor. Additionally, the reactivity of compound-13 was further confirmed by density functional theory analysis. The above findings suggest compound-13 to be a potential α-amylase inhibitor in DM. And setting the stage for further in vitro and in vivo experimental validation.

Computational Screening of FDA‐Approved Hepatitis C Drugs for Inhibition of VEGFR2 in Liver Cancer

Liver cancer (LC) is one of the most common tumours and the leading cause of cancer‐related death globally. Amidst the problems associated with existing treatments, such as hepatotoxicity, recurrence, drug resistance, and other adverse effects, researchers are under pressure to find alternatives. Towards a comprehensive rationalisation of the search for new anti‐LC drugs among approved ones, we employed an in‐silico approach to accelerate the selection of the most efficacious LC drugs. The FDA‐approved hepatitis C virus (HCV) drugs were docked with the LC protein using the AutoDock Vina software. Compared to the control compound, two FDA‐approved HCV drugs (DB09102 and DB09027) were selected based on their binding energies and interactions with the target protein, which showed comparable binding energies. Furthermore, these compounds were then subjected to molecular dynamic simulation, principle component analysis, and MMGBSA using the AMBER20 software, and the results showed stable complexes compared to the control complex. All things considered, this study will help the scientific community and society find a novel drug to treat LC.

Molecular Insights into Macromolecules Structure, Function, and Regulation

Macromolecules exhibit ordered structures and complex functions in an aqueous environment with strong thermodynamic fluctuations [...].

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

Insights from in silico exploration of major curcumin analogs targeting human dipeptidyl peptidase IV

The goal of this work is to use a variety of in-silico techniques to identify anti-diabetic agents against DPP-IV enzyme from five main curcumin analogues. To produce the successful molecules, five main curcumin analogues were docked into the active site of DPP-IV enzyme. In comparison to the control molecule (Saxagliptin, -6.9 kcal/mol), all the compounds have the highest binding affinity (-7.6 to -7.7 kcal/mol) for the DPP-IV enzyme. These compounds underwent further testing for studies on drug-likeness, pharmacokinetics, and acute toxicity to see the efficacy and safety of compounds. To assess the stability of the docking complex and the binding posture identified during the docking experiment, our study got THC as the lead compound, which was then exposed to 200 ns of molecular dynamic simulation and PCA analysis. Additionally, DFT calculations were conducted to determine the thermodynamic, molecular orbital, and electrostatic potential characteristics of lead compound. Overall, the lead chemical has shown strong drug-like properties, is non-toxic, and has a sizable affinity for the DPP-IV enzyme.

Diabetic wound healing of aloe vera major phytoconstituents through TGF-β1 suppression via in-silico docking, molecular dynamic simulation and pharmacokinetic studies

To restore the integrity of the skin and subcutaneous tissue, the wound healing process involves a complex series of well-orchestrated biochemical and cellular events. Due to the existence of various active components, accessibility and few side effects, some plant extracts and their phytoconstituents are recognised as viable options for wound healing agents. To find possible inhibitors of diabetic wound healing, four main constituents of aloe vera were identified from the literature. TGF-β1 and the compounds were studied using molecular docking to see how they interacted with the active site of target protein (PDB ID: 6B8Y). The pharmacokinetics investigation of the aloe emodin with the highest dock score complied with all the Lipinski's rule of five and pharmacokinetics criteria. Conformational change in the docked complex of Aloe emodin was investigated with the Amber simulation software, via a molecular dynamic (MD) simulation. The MD simulations of aloe emodin bound to TGF-β1 showed the significant structural rotations and twists occurring from 0 to 200 ns. The estimate of the aloe emodin-TGF-β1 complex's binding free energy has also been done using MM-PBSA/GBSA techniques. Additionally, aloe emodin has a wide range of enzymatic activities since their probability active (Pa) values is >0.700. 'Aloe emodin', an active extract of aloe vera, has been identified as the key chemical in the current investigation that can inhibit diabetic wound healing. Both in-vitro and in-vivo experiments will be used in a wet lab to confirm the current computational findings.Communicated by Ramaswamy H. Sarma.