Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29461
Publication type: | Article in scientific journal |
Type of review: | Peer review (publication) |
Title: | LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries |
Authors: | Patsch, David Eichenberger, Michael Voss, Moritz Bornscheuer, Uwe T. Buller, Rebecca M. |
et. al: | No |
DOI: | 10.1016/j.csbj.2023.09.013 10.21256/zhaw-29461 |
Published in: | Computational and Structural Biotechnology Journal |
Volume(Issue): | 21 |
Page(s): | 4488 |
Pages to: | 4496 |
Issue Date: | 2023 |
Publisher / Ed. Institution: | Elsevier |
ISSN: | 2001-0370 |
Language: | English |
Subjects: | Bioinformatic tool; Enzyme engineering; Library design; Sequence space |
Subject (DDC): | 004: Computer science 660.6: Biotechnology |
Abstract: | 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. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/29461 |
Fulltext version: | Published version |
License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
Departement: | Life Sciences and Facility Management |
Organisational Unit: | Institute of Chemistry and Biotechnology (ICBT) |
Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
File | Description | Size | Format | |
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2023_Patsch-etal_LibGENiE_CSBJ.pdf | 2.72 MB | Adobe PDF | View/Open |
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Patsch, D., Eichenberger, M., Voss, M., Bornscheuer, U. T., & Buller, R. M. (2023). LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries. Computational and Structural Biotechnology Journal, 21, 4488–4496. https://doi.org/10.1016/j.csbj.2023.09.013
Patsch, D. et al. (2023) ‘LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries’, Computational and Structural Biotechnology Journal, 21, pp. 4488–4496. Available at: https://doi.org/10.1016/j.csbj.2023.09.013.
D. Patsch, M. Eichenberger, M. Voss, U. T. Bornscheuer, and R. M. Buller, “LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries,” Computational and Structural Biotechnology Journal, vol. 21, pp. 4488–4496, 2023, doi: 10.1016/j.csbj.2023.09.013.
PATSCH, David, Michael EICHENBERGER, Moritz VOSS, Uwe T. BORNSCHEUER und Rebecca M. BULLER, 2023. LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries. Computational and Structural Biotechnology Journal. 2023. Bd. 21, S. 4488–4496. DOI 10.1016/j.csbj.2023.09.013
Patsch, David, Michael Eichenberger, Moritz Voss, Uwe T. Bornscheuer, and Rebecca M. Buller. 2023. “LibGENiE : A Bioinformatic Pipeline for the Design of Information-Enriched Enzyme Libraries.” Computational and Structural Biotechnology Journal 21: 4488–96. https://doi.org/10.1016/j.csbj.2023.09.013.
Patsch, David, et al. “LibGENiE : A Bioinformatic Pipeline for the Design of Information-Enriched Enzyme Libraries.” Computational and Structural Biotechnology Journal, vol. 21, 2023, pp. 4488–96, https://doi.org/10.1016/j.csbj.2023.09.013.
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