Pharmacoinformatic Screening of Natural Compounds to Identify Potential GPER1 Inhibitors

GPER1 belongs to the large GPCR family, known for its diverse biological functions and therapeutic value in multiple diseases. It has gained attention for its role in mediating estrogenic signaling pathways, which exert protective and beneficial effects in disease. The current study aims to identify potential compounds for effective therapies targeting GPER1 employing in silico approaches.

This study integrates a suite of computational techniques, including molecular modeling, pharmacophore-based virtual screening, docking studies, and molecular dynamics simulations, to analyze structural dynamics, functional relevance, and potential therapeutic applications. Ligand Scout was used to construct the pharmacophore models of reported inhibitors and drugs retrieved before the virtual screening. RMSD, RMSF, and B-factor graphs are generated through simulations for 50 nanoseconds to reveal the structural features of the GPER1 model.

The comparative modeling approach was applied to build the GPER1 3D model, yielding an overall quality factor of 89%. Energy minimization and simulations for structure optimization were followed until a 92% quality factor was attained. Two novel leads, ZINC39470612 and ZINC72326045, appeared to have maximum binding affinities, i.e., 63.4983 and 58.87, respectively, with the GPER1 receptor. Post-docking analyses identified Phe-98, Met-260, Tyr-324, and Leu-327 as crucial residues for the effective drug therapies targeting GPER1.

Extensive analyses have demonstrated that ZINC39470612 and ZINC72326045 are identified as potential compounds for targeting GPER1. These compounds also exhibited better binding affinities and ADME features than reported drugs and inhibitors. These in silico findings provide critical structural and functional insights with promising therapeutic relevance.