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Engineering of enzymes using non-natural amino acids

In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated in...

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Detalles Bibliográficos
Autores principales: Li, Yiwen, Dalby, Paul A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366748/
https://www.ncbi.nlm.nih.gov/pubmed/35856922
http://dx.doi.org/10.1042/BSR20220168
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author Li, Yiwen
Dalby, Paul A.
author_facet Li, Yiwen
Dalby, Paul A.
author_sort Li, Yiwen
collection PubMed
description In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated into proteins in a site-specific or residue-specific manner, which breaks the limit of 20 natural amino acids for protein engineering. Benefitting from this approach, numerous enzymes have been engineered with nnAAs for improved properties or extended functionality. In the present review, we focus on applications and strategies for using nnAAs in enzyme engineering. Notably, approaches to computational modelling of enzymes with nnAAs are also addressed. Finally, we discuss the bottlenecks that currently need to be addressed in order to realise the broader prospects of this genetic code extension technique.
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spelling pubmed-93667482022-08-24 Engineering of enzymes using non-natural amino acids Li, Yiwen Dalby, Paul A. Biosci Rep Biochemical Techniques & Resources In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated into proteins in a site-specific or residue-specific manner, which breaks the limit of 20 natural amino acids for protein engineering. Benefitting from this approach, numerous enzymes have been engineered with nnAAs for improved properties or extended functionality. In the present review, we focus on applications and strategies for using nnAAs in enzyme engineering. Notably, approaches to computational modelling of enzymes with nnAAs are also addressed. Finally, we discuss the bottlenecks that currently need to be addressed in order to realise the broader prospects of this genetic code extension technique. Portland Press Ltd. 2022-08-05 /pmc/articles/PMC9366748/ /pubmed/35856922 http://dx.doi.org/10.1042/BSR20220168 Text en © 2022 The Author(s). https://creativecommons.org/licenses/by/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . Open access for the present article was enabled by the participation of University College London in an all-inclusive Read & Publish agreement with Portland Press and the Biochemical Society under a transformative agreement with JISC.
spellingShingle Biochemical Techniques & Resources
Li, Yiwen
Dalby, Paul A.
Engineering of enzymes using non-natural amino acids
title Engineering of enzymes using non-natural amino acids
title_full Engineering of enzymes using non-natural amino acids
title_fullStr Engineering of enzymes using non-natural amino acids
title_full_unstemmed Engineering of enzymes using non-natural amino acids
title_short Engineering of enzymes using non-natural amino acids
title_sort engineering of enzymes using non-natural amino acids
topic Biochemical Techniques & Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366748/
https://www.ncbi.nlm.nih.gov/pubmed/35856922
http://dx.doi.org/10.1042/BSR20220168
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