Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

Manrique, Pedro D.; Leus, Inga V.; López, César A.; Mehla, Jitender; Malloci, Giuliano; Gervasoni, Silvia; Vargiu, Attilio V.; Kinthada, Rama K.; Herndon, Liam; Hengartner, Nicolas W.; Walker, John K.; Rybenkov, Valentin V.; Ruggerone, Paolo; Zgurskaya, Helen I.; Gnanakaran, S.

doi:10.1038/s42004-024-01161-y

Title: Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

Journal Article · 11 April 2024 · Communications Chemistry

DOI: https://doi.org/10.1038/s42004-024-01161-y · OSTI ID:2470486

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^[2]; López, César A. ^[3];

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^[4]; Vargiu, Attilio V. ^[4]; Kinthada, Rama K. ^[5]; Herndon, Liam ^[3]; Hengartner, Nicolas W. ^[3]; Walker, John K. ^[5];

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George Washington University, Washington, DC (United States)
University of Oklahoma, Norman, OK (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
University of Cagliari, Monserrato, CA (Italy)
Saint Louis University, MO (United States)

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Institute of Allergy and Infectious Diseases (NIAID); National Institutes of Health (NIH); U.S. Air Force Office of Scientific Research; USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2470486

Journal Information:: Communications Chemistry, Journal Name: Communications Chemistry Journal Issue: 1 Vol. 7; ISSN 2399-3669

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

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