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LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature’s synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, th...

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Detalles Bibliográficos
Autores principales: Patsch, David, Eichenberger, Michael, Voss, Moritz, Bornscheuer, Uwe T., Buller, Rebecca M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510078/
https://www.ncbi.nlm.nih.gov/pubmed/37736300
http://dx.doi.org/10.1016/j.csbj.2023.09.013
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author Patsch, David
Eichenberger, Michael
Voss, Moritz
Bornscheuer, Uwe T.
Buller, Rebecca M.
author_facet Patsch, David
Eichenberger, Michael
Voss, Moritz
Bornscheuer, Uwe T.
Buller, Rebecca M.
author_sort Patsch, David
collection PubMed
description Enzymes are potent catalysts with high specificity and selectivity. To leverage nature’s synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.
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spelling pubmed-105100782023-09-21 LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries Patsch, David Eichenberger, Michael Voss, Moritz Bornscheuer, Uwe T. Buller, Rebecca M. Comput Struct Biotechnol J Software/Web Server Article Enzymes are potent catalysts with high specificity and selectivity. To leverage nature’s synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow. Research Network of Computational and Structural Biotechnology 2023-09-14 /pmc/articles/PMC10510078/ /pubmed/37736300 http://dx.doi.org/10.1016/j.csbj.2023.09.013 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Software/Web Server Article
Patsch, David
Eichenberger, Michael
Voss, Moritz
Bornscheuer, Uwe T.
Buller, Rebecca M.
LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title_full LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title_fullStr LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title_full_unstemmed LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title_short LibGENiE – A bioinformatic pipeline for the design of information-enriched enzyme libraries
title_sort libgenie – a bioinformatic pipeline for the design of information-enriched enzyme libraries
topic Software/Web Server Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510078/
https://www.ncbi.nlm.nih.gov/pubmed/37736300
http://dx.doi.org/10.1016/j.csbj.2023.09.013
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