LibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme libraries

Patsch, David; Eichenberger, Michael; Voss, Moritz; Bornscheuer, Uwe T.; Buller, Rebecca M.

doi:10.1016/j.csbj.2023.09.013

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

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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.