Improving Enzyme Fitness with Machine Learning

Authors

  • David Patsch Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, CH-8820 Wädenswil; Institute of Biochemistry, Dept. of Biotechnology & Enzyme Catalysis, Greifswald University, Felix-Hausdorff-Strasse 4, D17487 Greifswald, Germany
  • Rebecca Buller Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, CH-8820 Wädenswil

DOI:

https://doi.org/10.2533/chimia.2023.116

PMID:

38047813

Keywords:

Bioinformatics, Enzyme engineering, Halogenase, Industrial biocatalysis, Machine learning

Abstract

The combinatorial composition of proteins has triggered the application of machine learning in enzyme engineering. By predicting how protein sequence encodes function, researchers aim to leverage machine learning models to select a reduced number of optimized sequences for laboratory measurement with the aim to lower costs and shorten timelines of enzyme engineering campaigns. In this review, we will highlight successful algorithm-aided protein engineering examples, including work carried out within the NCCR Catalysis. In this context, we will discuss the underlying computational methods developed to improve enzyme properties such as enantioselectivity, regioselectivity, activity, and stability. Considering the rapid maturing of computational techniques, we expect that their continued application in enzyme engineering campaigns will be key to deliver additional powerful biocatalysts for sustainable chemical synthesis.

Funding data

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Published

2023-03-29

How to Cite

[1]
D. Patsch, R. Buller, Chimia 2023, 77, 116, DOI: 10.2533/chimia.2023.116.

Issue

Section

Scientific Articles