The Effect of Lipopolysaccharides on the Electrostatic Properties of Gram‐Negative General Porins from Enterobacteriaceae

Ceccarelli, Matteo; Milenkovic, Stefan; Bodrenko, Igor V.

doi:10.1002/cphc.202400147

Cited by 1 publication

(2 citation statements)

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“…coli permeability through general porins . While other size-related descriptors have already been included so far, whose importance emerged in the previous sections of this manuscript, the dipole moment is of special interest because of its direct role in the interaction with the electrostatics of the porin lumen. ,, These bacterial protein channels have their characteristic internal electric field, with the molecular dipole’s interaction contributing to the transport process’s total free energy. This, in turn, will lead to a quantitative effect on both permeability and accumulation.…”

Section: Resultsmentioning

confidence: 99%

“…Permeability is a nonlocal property and relies on a statistical description, depending on an integral over a free energy surface . Studies on permeability through OMPs in Escherichia coli by Ceccarelli et al emphasized the significance of interactions between the translocating molecules and the transport medium. − These interactions offer insights to identify highly permeating molecules. Further, it was shown that the sole use of mean values for physicochemical molecular descriptors is not enough to catch their statistical variability.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Prediction of Small Molecule Accumulation in Escherichia Coli Enhanced with Descriptor Statistics

Milenkovic,

Boi,

Scorciapino

et al. 2024

J. Chem. Theory Comput.

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Antibiotic resistance, particularly among Gram-negative bacteria, poses a significant healthcare challenge due to their ability to evade antibiotic action through various mechanisms. In this study, we explore the prediction of small molecule accumulation in Gram-negative bacteria by using machine learning techniques enhanced with statistical descriptors derived from molecular dynamics simulations. We begin by identifying a minimal set of molecular descriptors that maximize the model's predictive power while preserving human interpretability. We optimize model accuracy, precision, and the area under the receiver operating characteristic curve through an iterative process. We demonstrate that the inclusion of statistical descriptors significantly improves model performance across various prediction metrics. Particularly, the addition of statistical descriptors related to dipole moment and minimum projection radius enhances the model's predictive capabilities, shedding light on the physicochemical properties crucial for small molecule accumulation. Our findings highlight the importance of considering statistical moments beyond mean values in predictive modeling and suggest avenues for future research. Overall, our study provides insights into the complex dynamics of antibiotic accumulation in Escherichia coli bacterial cells, generalizable to other Gram-negative species, offering a promising approach for the discovery of effective antibacterial agents, identifying new hits, and improving them to define effective lead agents.

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