Identification of human phosphoglycerate mutase 1 (PGAM1) inhibitors using hybrid virtual screening approaches

PGAM1 plays a critical role in cancer cell metabolism through glycolysis and different biosynthesis pathways to promote cancer. It is generally known as a crucial target for treating pancreatic ductal adenocarcinoma, the deadliest known malignancy worldwide. In recent years different studies have been reported that strived to find inhibitory agents to target PGAM1, however, no validated inhibitor has been reported so far, and only a small number of different inhibitors have been reported with limited potency at the molecular level. Our in silico studies aimed to identify potential new PGAM1 inhibitors that could bind at the allosteric sites. At first, shape and feature-based models were generated and optimized by performing receiver operating characteristic (ROC) based enrichment studies. The best query model was then employed for performing shape, color, and electrostatics complementarity-based virtual screening of the ChemDiv database. The top two hundred and thirteen hits with greater than 1.2 TanimotoCombo score were selected and then subjected to structure-based molecular docking studies. The hits yielded better docking scores than reported compounds, were selected for subsequent structural similarity-based clustering analysis to select the best hits from each cluster. Molecular dynamics simulations and binding free energy calculations were performed to validate their plausible binding modes and their binding affinities with the PGAM1 enzyme. The results showed that these compounds were binding in the reported allosteric site of the enzyme and can serve as a good starting point to design better active selective scaffolds against PGAM1enzyme.