Improving enzyme fitness with machine learning

Patsch, David; Buller, Rebecca

doi:10.2533/chimia.2023.116

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29459

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Improving enzyme fitness with machine learning
Authors:	Patsch, David Buller, Rebecca
et. al:	No
DOI:	10.2533/chimia.2023.116 10.21256/zhaw-29459
Published in:	Chimia
Volume(Issue):	77
Issue:	3
Page(s):	116
Pages to:	121
Issue Date:	29-Mar-2023
Publisher / Ed. Institution:	Swiss Chemical Society
ISSN:	0009-4293 2673-2424
Language:	English
Subjects:	Bioinformatics; Enzyme engineering; Halogenase; Industrial biocatalysis; Machine learning; Catalysis; Data collection; Machine learning; Algorithm; Engineering
Subject (DDC):	006: Special computer methods 660.6: Biotechnology
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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29459
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., & Buller, R. (2023). Improving enzyme fitness with machine learning. Chimia, 77(3), 116–121. https://doi.org/10.2533/chimia.2023.116

Patsch, D. and Buller, R. (2023) ‘Improving enzyme fitness with machine learning’, Chimia, 77(3), pp. 116–121. Available at: https://doi.org/10.2533/chimia.2023.116.

D. Patsch and R. Buller, “Improving enzyme fitness with machine learning,” Chimia, vol. 77, no. 3, pp. 116–121, Mar. 2023, doi: 10.2533/chimia.2023.116.

PATSCH, David und Rebecca BULLER, 2023. Improving enzyme fitness with machine learning. Chimia. 29 März 2023. Bd. 77, Nr. 3, S. 116–121. DOI 10.2533/chimia.2023.116

Patsch, David, and Rebecca Buller. 2023. “Improving Enzyme Fitness with Machine Learning.” Chimia 77 (3): 116–21. https://doi.org/10.2533/chimia.2023.116.

Patsch, David, and Rebecca Buller. “Improving Enzyme Fitness with Machine Learning.” Chimia, vol. 77, no. 3, Mar. 2023, pp. 116–21, https://doi.org/10.2533/chimia.2023.116.