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Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance

In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The pro...

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Autores principales: Michael, Eleni, Saint-Jalme, Rémy, Mignon, David, Simonson, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868620/
https://www.ncbi.nlm.nih.gov/pubmed/36699702
http://dx.doi.org/10.3389/fmolb.2022.905588
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author Michael, Eleni
Saint-Jalme, Rémy
Mignon, David
Simonson, Thomas
author_facet Michael, Eleni
Saint-Jalme, Rémy
Mignon, David
Simonson, Thomas
author_sort Michael, Eleni
collection PubMed
description In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme “vitality”, defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or “redesigned” individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.
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spelling pubmed-98686202023-01-24 Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance Michael, Eleni Saint-Jalme, Rémy Mignon, David Simonson, Thomas Front Mol Biosci Molecular Biosciences In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme “vitality”, defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or “redesigned” individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9868620/ /pubmed/36699702 http://dx.doi.org/10.3389/fmolb.2022.905588 Text en Copyright © 2023 Michael, Saint-Jalme, Mignon and Simonson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Michael, Eleni
Saint-Jalme, Rémy
Mignon, David
Simonson, Thomas
Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title_full Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title_fullStr Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title_full_unstemmed Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title_short Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
title_sort computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868620/
https://www.ncbi.nlm.nih.gov/pubmed/36699702
http://dx.doi.org/10.3389/fmolb.2022.905588
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