Structural insights into GluN2B-containing NMDA receptor antagonists: a computational approach

Abstract

GluN2B-containing NMDARs are related to neurodegenerative diseases, making the GluN2B-selective antagonist a promising drug candidate. Although several GluN2B antagonists have been developed, none have been approved for the market. Ifenprodil was the first potent GluN2B-selective antagonist, and its therapeutic use is limited due to its activity on serotonergic and adrenergic receptors. We provide a comprehensive analysis of GluN2B homologous proteins and their co-crystallized ligands using computational methods, including molecular docking, DFT calculations and molecular dynamics simulations here. This study identifies key binding modes of GluN2B antagonists, focusing on two pharmacophore models: the groups of Ifenprodil and EVT-101. It also elucidates the structure of GluN2B's V-type binding pocket and deep binding sites, identifying key residues that may influence its activity. These findings are crucial for the rational design of GluN2B antagonists.