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Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics

Chio, Hokin; Guest, Ellen E.; Hobman, Jon L.; Dottorini, Tania; Hirst, Jonathan D.; Stekel, Dov J.

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Hokin Chio

Ellen E. Guest

Associate Professor

Professor of Computational Biology


Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.


Chio, H., Guest, E. E., Hobman, J. L., Dottorini, T., Hirst, J. D., & Stekel, D. J. (2023). Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics. Journal of Molecular Graphics and Modelling, 123, Article 108508.

Journal Article Type Article
Acceptance Date May 4, 2023
Online Publication Date May 24, 2023
Publication Date Sep 1, 2023
Deposit Date May 16, 2023
Publicly Available Date May 17, 2023
Journal Journal of Molecular Graphics and Modelling
Print ISSN 1093-3263
Electronic ISSN 1873-4243
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 123
Article Number 108508
Keywords Penicillin; Metabolites resistance; Molecular docking; Molecular dynamics
Public URL
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