Cargando…
Modelling proteins’ hidden conformations to predict antibiotic resistance
TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in det...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477488/ https://www.ncbi.nlm.nih.gov/pubmed/27708258 http://dx.doi.org/10.1038/ncomms12965 |
_version_ | 1783244802905604096 |
---|---|
author | Hart, Kathryn M. Ho, Chris M. W. Dutta, Supratik Gross, Michael L. Bowman, Gregory R. |
author_facet | Hart, Kathryn M. Ho, Chris M. W. Dutta, Supratik Gross, Michael L. Bowman, Gregory R. |
author_sort | Hart, Kathryn M. |
collection | PubMed |
description | TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design. |
format | Online Article Text |
id | pubmed-5477488 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54774882017-07-03 Modelling proteins’ hidden conformations to predict antibiotic resistance Hart, Kathryn M. Ho, Chris M. W. Dutta, Supratik Gross, Michael L. Bowman, Gregory R. Nat Commun Article TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design. Nature Publishing Group 2016-10-06 /pmc/articles/PMC5477488/ /pubmed/27708258 http://dx.doi.org/10.1038/ncomms12965 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Hart, Kathryn M. Ho, Chris M. W. Dutta, Supratik Gross, Michael L. Bowman, Gregory R. Modelling proteins’ hidden conformations to predict antibiotic resistance |
title | Modelling proteins’ hidden conformations to predict antibiotic resistance |
title_full | Modelling proteins’ hidden conformations to predict antibiotic resistance |
title_fullStr | Modelling proteins’ hidden conformations to predict antibiotic resistance |
title_full_unstemmed | Modelling proteins’ hidden conformations to predict antibiotic resistance |
title_short | Modelling proteins’ hidden conformations to predict antibiotic resistance |
title_sort | modelling proteins’ hidden conformations to predict antibiotic resistance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477488/ https://www.ncbi.nlm.nih.gov/pubmed/27708258 http://dx.doi.org/10.1038/ncomms12965 |
work_keys_str_mv | AT hartkathrynm modellingproteinshiddenconformationstopredictantibioticresistance AT hochrismw modellingproteinshiddenconformationstopredictantibioticresistance AT duttasupratik modellingproteinshiddenconformationstopredictantibioticresistance AT grossmichaell modellingproteinshiddenconformationstopredictantibioticresistance AT bowmangregoryr modellingproteinshiddenconformationstopredictantibioticresistance |