3D-QSAR study, docking molecular and simulation dynamic on series of benzimidazole derivatives as anti-cancer agents

Unlocking Benzosampangine's Potential: A Computational Approach to Investigating, Its Role as a PD-L1 Inhibitor in Tumor Immune Evasion via Molecular Docking, Dynamic Simulation, and ADMET Profiling

The interaction between programmed cell death protein 1 (PD-1) and its ligand PD-L1 plays a crucial role in tumor immune evasion, presenting a critical target for cancer immunotherapy. Despite being effective, current monoclonal antibodies present some drawbacks such as high costs, toxicity, and resistance development. Therefore, the development of small-molecule inhibitors is necessary, especially those derived from natural sources. In this study, benzosampangine is predicted as a promising PD-L1 inhibitor, with potential applications in cancer immunotherapy. Utilizing the high-resolution crystal structure of human PD-L1 (PDB ID: 5O45), we screened 511 natural compounds, identifying benzosampangine as a top candidate with exceptional inhibitory properties. Molecular docking predicted that benzosampangine exhibits a strong binding affinity for PD-L1 (-9.4 kcal/mol) compared with established controls such as CA-170 (-6.5 kcal/mol), BMS-202 (-8.6 kcal/mol), and pyrvinium (-8.9 kcal/mol). The compound's predicted binding efficacy is highlighted by robust interactions with key amino acids (ILE54, TYR56, GLN66, MET115, ILE116, SER117, ALA121, ASP122) within the active site, notably forming 3 Pi-sulfur interactions with MET115-an interaction absents in control inhibitors. In addition, ADMET profiling suggests that over the control molecules, benzosampangine has several key advantages, including favorable solubility, permeability, metabolic stability, and low toxicity, while adhering to Lipinski's rule of five. Molecular dynamic simulations predict the stability of the benzosampangine-PD-L1 complex, reinforcing its potential to sustain inhibition of the PD-1/PD-L1 pathway. MMGBSA analysis calculated a binding free energy (ΔGbind) of -39.39 kcal/mol for the benzosampangine-PD-L1 complex, with significant contributions from Coulombic, lipophilic, and Van der Waals interactions, validating the predicted docking results. This study investigates in silico benzosampangine, predicting its better molecular interactions and pharmacokinetic profile compared with several already known PD-L1 inhibitors.

Discover the Medication Potential of Algerian Medicinal Plants Against Sars-Cov-2 Main Protease (Mpro): Molecular Docking, Molecular Dynamic Simulation, and ADMET Analysis

At the end of 2019, the world faced a big challenge and crisis caused by the SARS-CoV-2 virus. It spreads rapidly and is contagious; no treatment has officially been found. Algeria has used medicinal plants native to the country to defend against this pandemic. The objective of this paper is based on a molecular docking study of the active compounds of five Algerian medicinal plants with their target Sars-2Cov-2 virus protease to assess their potential antiviral activity against COVID-19. Innovative software and computerized databases were introduced into the in-silico domain, mainly the Auto-Dock software version 1.5.6. Similar results were obtained for all ligands, with a better chemical affinity of − 5.600 kcal/mol for the protease target 6LU7 and − 5.700 kcal/mol for the protease target 6WTT, with an average of − 4.227 kcal/mol and − 4.221 kcal/mol, respectively. The protease targets 6LU7 and 6WTT. In the ADME-Tox study, the active compounds of Algerian medicinal plants also demonstrated an excellent pharmacokinetic and toxic profile. Best scores were noted for cedrol, camphor, and eucalyptol. A molecular dynamics simulation showed the stability of camphor-6LU7 and cedrol-6LU7 complexes, favoring the biological potential of white artemisia and cypress plants.

Profiling the Structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC₅₀) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.