Pharmacophore-Based Study: An In Silico Perspective for the Identification of Potential New Delhi Metallo-β-lactamase-1 (NDM-1) Inhibitors

In the ongoing battle against antibiotic-resistant bacteria, New Delhi metallo-β-lactamase-1 (NDM-1) has emerged as a significant therapeutic challenge due to its ability to confer resistance to a broad range of β-lactam antibiotics. This study presents a pharmacophore-based virtual screening, docking, and molecular dynamics simulation approach for the identification of potential inhibitors targeting NDM-1, a critical enzyme associated with antibiotic resistance. Through the generation of a pharmacophore model and subsequent virtual screening of compound libraries, candidate molecules (ZINC29142850 (Z1), ZINC78607001 (Z2), and ZINC94303138 (Z3)) were prioritized based on their similarity to known NDM-1 binder (hydrolyzed oxacillin (0WO)). Molecular docking studies further elucidated the binding modes and affinities of the selected compounds towards the active site of NDM-1. These compounds demonstrated superior binding affinities to the enzyme compared to a control compound (−7.30 kcal/mol), with binding scores of −7.13, −7.92, and −8.10 kcal/mol, respectively. Binding interactions within NDM-1’s active site showed significant interactions with critical residues such as His250, Asn220, and Trp93 for these compounds. Subsequent molecular dynamics simulations were conducted to assess the stability of the ligand–enzyme complexes, showing low root mean square deviation (RMSD) values between 0.5 and 0.7 nm for Z1, Z2, which indicate high stability. Z2’s compactness in principal component analysis (PCA) suggests that it can stabilize particular protein conformations more efficiently. Z2 displays a very cohesive landscape with a notable deep basin, suggesting a very persistent conformational state induced by the ligand, indicating robust binding and perhaps efficient inhibition. Z2 demonstrates the highest binding affinity among the examined compounds with a binding free energy of −25.68 kcal/mol, suggesting that it could offer effective inhibition of NDM-1. This study highlights the efficacy of computational tools in identifying novel antimicrobial agents against resistant bacteria, accelerating drug discovery processes.