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Predicting allosteric mutants that increase activity of a major antibiotic resistance enzyme
Univ Virginia, Dept Mol Physiol..
Department of Molecular Physiology, University of Virginia; Department of Biomedical Engineering, University of Virginia.
Univ Virginia, Dept Mol Physiol..
Univ Virginia, Dept Mol Physiol..
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2017 (English)In: Chemical Science, ISSN 2041-6520, E-ISSN 2041-6539, Vol. 8, no 9, p. 6484-6492Article in journal (Refereed) Published
Abstract [en]

The CTX-M family of beta lactamases mediate broad-spectrum antibiotic resistance and are present in the majority of drug-resistant Gram-negative bacterial infections worldwide. Allosteric mutations that increase catalytic rates of these drug resistance enzymes have been identified in clinical isolates but are challenging to predict prospectively. We have used molecular dynamics simulations to predict allosteric mutants increasing CTX-M9 drug resistance, experimentally testing top mutants using multiple antibiotics. Purified enzymes show an increase in catalytic rate and efficiency, while mutant crystal structures show no detectable changes from wild-type CTX-M9. We hypothesize that increased drug resistance results from changes in the conformational ensemble of an acyl intermediate in hydrolysis. Machine-learning analyses on the three top mutants identify changes to the binding-pocket conformational ensemble by which these allosteric mutations transmit their effect. These findings show how molecular simulation can predict how allosteric mutations alter active-site conformational equilibria to increase catalytic rates and thus resistance against common clinically used antibiotics.

Place, publisher, year, edition, pages
Cambridge: Royal Soc Chemistry , 2017. Vol. 8, no 9, p. 6484-6492
Keywords [en]
A Beta-Lactamases, Molecular-Dynamics Simulations, Ctx-M Enzymes, Mutual Information, High-Throughput, Force-Field, Evolution, Binding, Mutagenesis, Cefotaxime
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-330543DOI: 10.1039/c7sc02676eISI: 000408168600071OAI: oai:DiVA.org:uu-330543DiVA, id: diva2:1146633
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GoogleAvailable from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-10-03

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