Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development

Authors

  • Sumire Honda Malca Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, CH-8820 Wädenswil, Switzerland
  • Peter Stockinger Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, CH-8820 Wädenswil, Switzerland
  • Nadine Duss Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, CH-8820 Wädenswil, Switzerland
  • Daniela Milbredt Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, CH-8820 Wädenswil, Switzerland
  • Hans Iding Process Chemistry & Catalysis, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
  • Rebecca Buller Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, Einsiedlerstrasse 31, CH-8820 Wädenswil, Switzerland

DOI:

https://doi.org/10.2533/chimia.2024.108

PMID:

38547011

Keywords:

Automation, Biocatalysis, Bioinformatics, Enzyme engineering, Machine learning, Pharmaceutical industry

Abstract

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.

Funding data

CHIMIA Vol. 78 No. 3 (2024): Academic / Industrial Collaborations in Process Chemistry & Technology

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Published

2024-03-27

Issue

Vol. 78 No. 3 (2024): Academic / Industrial Collaborations in Process Chemistry & Technology

Section

Scientific Articles

License

Copyright (c) 2024 Sumire Honda Malca, Peter Stockinger, Nadine Duss, Daniela Milbredt, Hans Iding, Rebecca Buller

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chimia 2024, 78, 108, DOI: 10.2533/chimia.2024.108.