New Study Reveals Breakthrough Antibacterial Treatment Targeting Staphylococcus aureus and Escherichia coli DNA Gyrase

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Dynamic profiling and binding affinity prediction of NBTI antibacterials against DNA gyrase enzyme by multidimensional machine learning and molecular dynamics simulations:
Bacterial type II topoisomerases are well-characterized and clinically important targets for antibacterial chemotherapy. Novel bacterial topoisomerase inhibitors (NBTIs) are a newly disclosed class of antibacterials. Prediction of their binding affinity to these enzymes would be beneficial for de novo design/optimization of new NBTIs. Utilizing in vitro NBTI experimental data, comprehensive multidimensional DNA gyrase surrogate models for Staphylococcus aureus (q$^2$ = 0.791) and Escherichia coli (q$^2$ = 0.806) were constructed. Both models accurately predicted the IC$_{50}$s of 26 NBTIs. To investigate the NBTI’s dynamic profile and binding to both targets, 10 selected NBTIs underwent molecular dynamics (MD) simulations. The analysis of MD production trajectories confirmed key hydrogen-bonding and hydrophobic contacts established by NBTIs in both enzymes. Binding free energies of selected NBTIs were computed using the linear interaction energy (LIE) method. This approach successfully predicts the binding free energies of NBTIs against S. aureus and E. coli DNA gyrase. The integrated modeling approach could prove valuable in de novo design and optimization of efficient NBTIs to combat resistant bacterial pathogens.

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Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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