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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29461
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dc.contributor.authorPatsch, David-
dc.contributor.authorEichenberger, Michael-
dc.contributor.authorVoss, Moritz-
dc.contributor.authorBornscheuer, Uwe T.-
dc.contributor.authorBuller, Rebecca M.-
dc.date.accessioned2024-01-04T13:00:18Z-
dc.date.available2024-01-04T13:00:18Z-
dc.date.issued2023-
dc.identifier.issn2001-0370de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29461-
dc.description.abstractEnzymes 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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofComputational and Structural Biotechnology Journalde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectBioinformatic toolde_CH
dc.subjectEnzyme engineeringde_CH
dc.subjectLibrary designde_CH
dc.subjectSequence spacede_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc660.6: Biotechnologiede_CH
dc.titleLibGENiE : a bioinformatic pipeline for the design of information-enriched enzyme librariesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Chemie und Biotechnologie (ICBT)de_CH
dc.identifier.doi10.1016/j.csbj.2023.09.013de_CH
dc.identifier.doi10.21256/zhaw-29461-
dc.identifier.pmid37736300de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end4496de_CH
zhaw.pages.start4488de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume21de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf180544de_CH
zhaw.webfeedBiokatalysede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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