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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hart, Kathryn M., Ho, Chris M. W., Dutta, Supratik, Gross, Michael L., Bowman, Gregory R.
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