Computational screening of natural and natural-like compounds to identify novel ligands for sigma-2 receptor

Sigma-2 (σ₂) receptor is a transmembrane protein shown to be linked with neurodegenerative diseases and cancer development. Thus, it emerges as a potential biological target for the advancement of anticancer and anti-Alzheimer's agents. The current study was aimed to identify potential σ₂ receptor ligands using integrated computational approaches including homology modelling, combined pharmacophore- and docking-based virtual screening, and molecular dynamics (MD) simulation. Pharmacophore-based screening was conducted against a database composed of 20,523 small natural and natural-like products. In total, 1200 structures were found to satisfy the required pharmacophore features and were then exposed to docking-based screening against the generated homology model of σ₂ receptor. On the basis of the pharmacophore fit scores, docking scores, and mechanism of binding interaction, 20 potential hits were retained. Five promising candidates were selected (SR84, SR823, SR300, SR413, and SR530) on the basis of their binding score and interaction. Further, in silico ADMET profiling of these compounds showed that the selected compounds possess favourable ADME properties with low toxicity risk. The mechanism of interaction of these compounds with σ₂ receptor as well as their binding stability were characterized by MD simulation.