Background: The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in regulating excitatory glutamatergic neurotransmission and synaptic plasticity. However, ex-cessive NMDAR activation can lead to increased calcium ion influx, resulting in excitotoxi-city—a key contributor to neurodegenerative diseases. Although current NMDAR inhibitors exist, their clinical use is limited due to adverse effects. Methods: This study employed computational screening of Bacillus-derived macrolactins to identify potential NMDAR antagonists. Molecular docking simulations were performed using AMDock v1.5.2 with the AutoDock Vina engine to assess binding affinities to NMDAR (PDB:7SAD). Docked complexes were analyzed for chemical interactions, including polar contacts, using PyMol v2 and Discovery Studio Visualizer v4.5. Pharmacokinetic properties of macrolactins were predicted using Deep-PK. Molecular dynamics simulations via GROMACS assessed complex stability through RMSD, RMSF, radius of gyration (Rg), hy-drogen bond count, and solvent-accessible surface area (SASA). Network pharmacology anal-ysis of macrolactins in Alzheimer’s disease (AD) involved mapping target interactions in STRING, importing into Cytoscape, and identifying hub genes using CytoHubba for KEGG pathway enrichment. Results: Macrolactin F emerged as a promising candidate, exhibiting strong binding affinity (-6.8 kcal/mol) and an estimated Ki of 10.37 μM, outperforming commercial memantine and other macrolactins. Molecular dynamics simulations confirmed the stability and conforma-tional integrity of the Macrolactin F–NMDAR complex. KEGG pathway enrichment analysis highlighted key hub pathways associated with AD, including hsa05010, hsa04725, hsa04722, hsa04071, hsa04068, hsa04150, and hsa04910. Discussion: The findings suggest that Macrolactin F possesses superior antagonistic activity against NMDAR compared to memantine, supported by molecular docking and dynamic sim-ulations. Network pharmacology analyses indicate that Macrolactin F can modulate critical signaling pathways implicated in AD, including PI3K/Akt/mTOR and MAPK cascades. Conclusion: Computational analyses identify Macrolactin F as a promising preclinical candi-date for developing allosteric NMDAR inhibitors. This aligns with SDG 3 by contributing to potential therapeutics for neurodegenerative diseases such as Alzheimer’s disease and supports SDG 10 by promoting accessible interventions to reduce global health disparities.
Rajendran et al. (Tue,) studied this question.