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Computational remodeling of an enzyme conformational landscape for altered substrate selectivity
Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539519/ https://www.ncbi.nlm.nih.gov/pubmed/37770431 http://dx.doi.org/10.1038/s41467-023-41762-0 |
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author | St-Jacques, Antony D. Rodriguez, Joshua M. Eason, Matthew G. Foster, Scott M. Khan, Safwat T. Damry, Adam M. Goto, Natalie K. Thompson, Michael C. Chica, Roberto A. |
author_facet | St-Jacques, Antony D. Rodriguez, Joshua M. Eason, Matthew G. Foster, Scott M. Khan, Safwat T. Damry, Adam M. Goto, Natalie K. Thompson, Michael C. Chica, Roberto A. |
author_sort | St-Jacques, Antony D. |
collection | PubMed |
description | Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity. |
format | Online Article Text |
id | pubmed-10539519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105395192023-09-30 Computational remodeling of an enzyme conformational landscape for altered substrate selectivity St-Jacques, Antony D. Rodriguez, Joshua M. Eason, Matthew G. Foster, Scott M. Khan, Safwat T. Damry, Adam M. Goto, Natalie K. Thompson, Michael C. Chica, Roberto A. Nat Commun Article Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity. Nature Publishing Group UK 2023-09-28 /pmc/articles/PMC10539519/ /pubmed/37770431 http://dx.doi.org/10.1038/s41467-023-41762-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article St-Jacques, Antony D. Rodriguez, Joshua M. Eason, Matthew G. Foster, Scott M. Khan, Safwat T. Damry, Adam M. Goto, Natalie K. Thompson, Michael C. Chica, Roberto A. Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title | Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title_full | Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title_fullStr | Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title_full_unstemmed | Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title_short | Computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
title_sort | computational remodeling of an enzyme conformational landscape for altered substrate selectivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539519/ https://www.ncbi.nlm.nih.gov/pubmed/37770431 http://dx.doi.org/10.1038/s41467-023-41762-0 |
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