Discovery of Natural Compound-Based Lead Molecule against Acetyltransferase Type 1 Bacterial Enzyme from Morganella morgani Using Machine Learning-Enabled Molecular Dynamics Simulation

Abstract

Drug-resistant Morganella morganii, a rod-shaped, Gram-negative, facultatively anaerobic bacillus belonging to the Enterobacteriaceae family, is a growing worldwide health concern due to its association with high morbidity and mortality rates. Recent advancements in machine learning, particularly Alphafold 2’s protein structure prediction using local physics and pattern recognition, have aided research efforts. This study focuses on the enzymatic activity of aminoglycoside N6′-acetyltransferase (aacA7), a critical transferase enzyme in bacteria that confers resistance to aminoglycosides. AacA7 modifies aminoglycoside molecules by catalyzing the acetylation of their 6′-amino group using acetyl-CoA, rendering antibiotics like kanamycin, neomycin, tobramycin, and amikacin inactive. We propose that Doripenem and OncoglabrinolC can interact with aacA7, potentially modifying its enzymatic activity. Molecular docking analysis of aacA7 with 22 drug targets revealed OncoglabrinolC as the most promising candidate, exhibiting a binding energy of −12.82 kcal/mol. These two top candidates, OncoglabrinolC and Doripenem, were then subjected to 100 ns of molecular dynamic simulations to assess their dynamic conformational features. Furthermore, the PredictSNP consensus classifier was used to predict the impact of mutations on aacA7 protein functionality. The study also investigated the interaction of wild-type and mutant aacA7 proteins with both Doripenem and OncoglabrinolC. These findings provide valuable insights into the binding behavior of OncoglabrinolC and Doripenem as potential lead molecules for repurposing against aacA7, potentially reducing the pathogenicity of Morganella morganii.