Cargando…
Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models
[Image: see text] Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein’s stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relat...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American
Chemical Society
2017
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746865/ https://www.ncbi.nlm.nih.gov/pubmed/29296672 http://dx.doi.org/10.1021/acscentsci.7b00465 |
_version_ | 1783289185924284416 |
---|---|
author | Zimmerman, Maxwell I. Hart, Kathryn M. Sibbald, Carrie A. Frederick, Thomas E. Jimah, John R. Knoverek, Catherine R. Tolia, Niraj H. Bowman, Gregory R. |
author_facet | Zimmerman, Maxwell I. Hart, Kathryn M. Sibbald, Carrie A. Frederick, Thomas E. Jimah, John R. Knoverek, Catherine R. Tolia, Niraj H. Bowman, Gregory R. |
author_sort | Zimmerman, Maxwell I. |
collection | PubMed |
description | [Image: see text] Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein’s stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relative populations. For example, the M182T substitution in TEM β-lactamase, an enzyme that confers antibiotic resistance to bacteria, is stabilizing but the precise mechanism remains unclear. Here, we employ Markov state models (MSMs) to uncover how M182T shifts the distribution of different structures that TEM adopts. We find that M182T stabilizes a helix that is a key component of a domain interface. We then predict the effects of other mutations, including a novel stabilizing mutation, and experimentally test our predictions using a combination of stability measurements, crystallography, NMR, and in vivo measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design. |
format | Online Article Text |
id | pubmed-5746865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | American
Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-57468652018-01-02 Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models Zimmerman, Maxwell I. Hart, Kathryn M. Sibbald, Carrie A. Frederick, Thomas E. Jimah, John R. Knoverek, Catherine R. Tolia, Niraj H. Bowman, Gregory R. ACS Cent Sci [Image: see text] Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein’s stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relative populations. For example, the M182T substitution in TEM β-lactamase, an enzyme that confers antibiotic resistance to bacteria, is stabilizing but the precise mechanism remains unclear. Here, we employ Markov state models (MSMs) to uncover how M182T shifts the distribution of different structures that TEM adopts. We find that M182T stabilizes a helix that is a key component of a domain interface. We then predict the effects of other mutations, including a novel stabilizing mutation, and experimentally test our predictions using a combination of stability measurements, crystallography, NMR, and in vivo measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design. American Chemical Society 2017-11-21 2017-12-27 /pmc/articles/PMC5746865/ /pubmed/29296672 http://dx.doi.org/10.1021/acscentsci.7b00465 Text en Copyright © 2017 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Zimmerman, Maxwell I. Hart, Kathryn M. Sibbald, Carrie A. Frederick, Thomas E. Jimah, John R. Knoverek, Catherine R. Tolia, Niraj H. Bowman, Gregory R. Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models |
title | Prediction of New Stabilizing Mutations Based on Mechanistic
Insights from Markov State Models |
title_full | Prediction of New Stabilizing Mutations Based on Mechanistic
Insights from Markov State Models |
title_fullStr | Prediction of New Stabilizing Mutations Based on Mechanistic
Insights from Markov State Models |
title_full_unstemmed | Prediction of New Stabilizing Mutations Based on Mechanistic
Insights from Markov State Models |
title_short | Prediction of New Stabilizing Mutations Based on Mechanistic
Insights from Markov State Models |
title_sort | prediction of new stabilizing mutations based on mechanistic
insights from markov state models |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746865/ https://www.ncbi.nlm.nih.gov/pubmed/29296672 http://dx.doi.org/10.1021/acscentsci.7b00465 |
work_keys_str_mv | AT zimmermanmaxwelli predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT hartkathrynm predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT sibbaldcarriea predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT frederickthomase predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT jimahjohnr predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT knoverekcatheriner predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT tolianirajh predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels AT bowmangregoryr predictionofnewstabilizingmutationsbasedonmechanisticinsightsfrommarkovstatemodels |