# Dynamic Profiling and Binding Affinity Prediction of NBTI Antibacterials against DNA Gyrase Enzyme by Multidimensional Machine Learning and Molecular Dynamics Simulations

## TL;DR

This paper combines machine learning and simulations to predict how well new antibacterial drugs bind to bacterial enzymes, aiding in drug design.

## Contribution

A novel integrated approach combining multidimensional modeling and molecular dynamics simulations for NBTI binding prediction.

## Key findings

- Accurate DNA gyrase models for S. aureus (q2 = 0.791) and E. coli (q2 = 0.806) predict NBTI IC50s.
- MD simulations reveal key hydrogen-bonding and hydrophobic interactions of NBTIs with DNA gyrase.
- LIE method with custom parameters successfully computes binding free energies for Gram-positive and Gram-negative DNA gyrase.

## Abstract

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, we constructed two comprehensive
multidimensional DNA gyrase surrogate models for Staphylococcus
aureus (q2 = 0.791) and Escherichia coli (q2 =
0.806). Both models accurately predicted the IC50s of 26
NBTIs from our recent studies. 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 that NBTIs
establish in both enzymes. Moreover, the binding free energies of
selected NBTIs were computed by the linear interaction energy (LIE)
method employing an in-house derived set of fitting parameters (α
= 0.16, β = 0.029, γ = 0.0, and intercept = −1.72),
which are successfully applicable to DNA gyrase of Gram-positive/Gram-negative
pathogens. Both methods offer accurate predictions of the binding
free energies of NBTIs against S. aureus and E. coli DNA gyrase. We are confident
that this integrated modeling approach could be valuable in the de novo design and optimization of efficient NBTIs for combating
resistant bacterial pathogens.