AI-optimized sanguinarine derivatives inhibiting sortase A for combating AmpC β-lactamase resistance in Enterobacter cloacae: An integrated computational approach.

The antimicrobial resistance is a serious health problem worldwide, and one of the causes of multidrug resistance is AmpC β-lactamase-producing Enterobacter cloacae. This work focused on analyzing the antibacterial properties of the plant-derived alkaloid sanguinarine and improve its derivatives with the help of artificial intelligence (AI) that may enhance antimicrobial activity. Therefore, molecular docking results demonstrate that AI-optimized Ligand 1 displayed the strongest binding affinity of -9.7 kcal/mol (AutoDock Vina) and -166.94 kcal/mol (HDOCK). Compared to Sanguinarine with a binding affinity of -9.2 kcal/mol. The lead AI-optimized Sanguinarine derivative with stable binding and good energetics was confirmed in molecular dynamics and in MMGBSA/MMPBSA analyses, suggesting that it may be a promising lead of AmpC β-lactamase inhibitors. Density Functional Theory (DFT) computations revealed that the lead AI-optimized compound had the HOMO-LUMO gap of 0.17089 eV and indicated moderate reactivity that would as a result of analysing the pharmacophore, key aromatic, hydrogen bond acceptor and hydrophobic sites were identified and the AI-optimized derivative was found to be a better drug-like assembly than natural Sanguinarine. The ADMET analysis showed potential lipophilicity, whole-GI absorption, and BBB permeability of the AI-optimized derivative and decreased toxicity in general, especially regarding neurotoxicity. The results indicate the possible improvement of the resistance to antibiotics using AI optimization that can aid in promoting the antimicrobial activity and safety set of Sanguinarine, which is a particularly promising additional tool that can be used to combat antibiotic resistance. These findings require further in vivo studies to validate the computational predictions that should prove their validity and confirm the possibility of using the results of trials with AI-optimized derivatives in clinical practice.