Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, ADMET, and Molecular Dynamics (MD) Simulation of Potential Inhibitors of PD-L1 from the Library of Marine Natural Products

Abstract

Background: In the past decade, several antibodies directed against the PD-1/PD-L1 interaction have been approved. However, therapeutic antibodies also exhibit some shortcomings. Using small molecules to regulate the PD-1/PD-L1 pathway may be another way to mobilize the immune system to fight cancer. Method: 52,765 marine natural products were screened against PD-L1(PDBID: 6R3K). To identify natural compounds, a structure-based pharmacophore model was generated, following by virtual screening and molecular docking. Then, the absorption, distribution, metabolism, and excretion (ADME) test was carried out to select the most suitable compounds. Finally, molecular dynamics simulation was also performed to validate the binding property of the top compound. Results: Initially, 13 small marine molecules were screened based on the pharmacophore model. Then, two compounds were selected for further evaluation based on the molecular docking scores. After ADME and toxicity studies, molecule 51320 was selected for further verification. By molecular dynamics analysis, molecule 51320 maintains a stable conformation with the target protein, so it has the chance to become an inhibitor of PD-L1. Conclusions: Through structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET approaches, and molecular dynamics (MD) simulation, the marine natural compound 51320 can be used as a small molecule inhibitor of PD-L1.

Cu (Ι) catalyzed A3 cascade coupling via C‐H functionalization followed by cyclization: Synthesis, in silico, in vitro, and toxicity studies of imidazo[2,1‐b]thiazoles

“…It is mainly dependent upon the predicted median lethal dose (LD50 value, in mg kg À1 body weight), means the dose at which 50% of test subjects vanishes on revelation to a compound and recognizes the toxic remains of loaded molecules and predictable toxicity targets. [41,42] In accordance to ProTox-II server, the predicted lethal dose LD50 for our target molecules is above 2000 mg kg À1 and less than 5000 mg kg À1 for all the tested compounds except 7t as shown in Table 8. Every single synthesized molecule belongs to the category of Class V apart from compound 7t that falls in Class IV category and there are no toxic remains present as asserted by the developer's restrictions, which is relatively superior when compared with the standard drug Fluconazole and Streptomycin, belonging to Class IV and Class III category, respectively.…”

“…It is mainly dependent upon the predicted median lethal dose (LD50 value, in mg kg −1 body weight), means the dose at which 50% of test subjects vanishes on revelation to a compound and recognizes the toxic remains of loaded molecules and predictable toxicity targets. [ 41,42 ]…”

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

“…Four pharmacophore models containing the essence of the corresponding ligand characteristics were generated by applying default settings. We hand-picked a set of 23 active compounds having the chemistry supporting cooperative binding to PLpro and experimental inhibition constant, IC 50 in the range of 0.1 to 5.7 μM from the literature to evaluate the selectivity index, i.e., the number of actives retrieved to the total number of actives ( Luo et al, 2021 ; Shen et al, 2021). During this process of evaluation, the pharmacophoric features of all 4 models were manually optimized to improve the number of actives retrieved.…”

“…We varied the features by increasing or decreasing the tolerance, and weightage, adding or excluding exclusion volume spheres, and omitting a minimum number of features to increase the sensitivity of the models ( Opo et al, 2021 ). The optimum pharmacophore model is the one that identified maximum hits among the actives, which was considered for further study ( Luo et al, 2021 ).…”

The Inhibitors of CDK4/6 from a Library of Marine Compound Database: A Pharmacophore, ADMET, Molecular Docking and Molecular Dynamics Study

“…It is worth mentioning that some studies also use the QSAR model to screen a marine natural product, and our pharmacophore modeling was based on the selected known inhibitors, instead of structured modeling on the complex. The marine natural product compounds obtained from the pharmacophore model generated by this method can be more appropriate to the known CDK4/6 inhibitors in terms of conformation [ 32 , 33 ]. Then we carried out the ADMET test, and finally obtained 20 compounds that showed good ADMET characteristics.…”