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  Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?

Li, G., Dong, Y., & Reetz, M. T. (2019). Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes? Advanced Synthesis & Catalysis, 361(11), 2377-2386. doi:10.1002/adsc.201900149.

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 Creators:
Li, Guangyue1, Author
Dong, Yijie1, Author
Reetz, Manfred T.2, 3, Author              
Affiliations:
1State Key Laboratory for Biology of Plant Diseases and Insect Pests/Key, Laboratory of Control of Biological Hazard Factors (Plant Origin), for Agri-product Quality and Safety, Ministry of Agriculture, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, 100081 People's Republic of China, ou_persistent22              
2Research Department Reetz, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445588              
3Fachbereich Chemie der Philipps-Universität, Hans-Meerwein-Strasse, 35032 Marburg, Germany, ou_persistent22              

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Free keywords: directed evolution; enzymes; machine learning; saturation mutagenesis; stereoselectivity
 Abstract: Machine learning as a form of artificial intelligence consists of algorithms and statistical models for improving computer performance for different tasks. Training data are utilized for making decisions and predictions. Since directed evolution of enzymes produces huge amounts of potential training data, machine learning seems to be ideally suited to support this protein engineering technique. Machine learning has been used in protein science for a long time with different purposes. This mini‐review focuses on the utility of machine learning as an aid in the directed evolution of selective enzymes. Recent studies have shown that the algorithms ASRA and Innov'SAR are well suited as guides when performing saturation mutagenesis at sites lining the binding pocket for enhancing stereoselectivity and activity.

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Language(s): eng - English
 Dates: 2019-01-312019-03-192019-06-06
 Publication Status: Published in print
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/adsc.201900149
 Degree: -

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Title: Advanced Synthesis & Catalysis
  Abbreviation : Adv. Synth. Catal.
Source Genre: Journal
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Publ. Info: Weinheim, Fed. Rep. of Germany : Wiley-VCH Verlag GmbH
Pages: - Volume / Issue: 361 (11) Sequence Number: - Start / End Page: 2377 - 2386 Identifier: ISSN: 1615-4150
CoNE: https://pure.mpg.de/cone/journals/resource/958634688013