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Machine Learning in Enzyme Engineering

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Mazurenko, Stanislav, Prokop, Zbynek, Damborsky, Jiri·2020·ACS Catalysis
DOI 10.1021/acscatal.9b04321
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Abstract

Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts.

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Cited by

Metadata

Authors
Mazurenko, S., Prokop, Z., & Damborsky, J.
Venue
ACS Catalysis
Publisher
American Chemical Society (ACS)
Published
2019-12-13
Volume
10
Issue
2
Pages
1210-1223
ISSN
2155-5435 (ISSN-L)
DOI
10.1021/acscatal.9b04321
OpenAlex
W2995106228
Cited by
410
References
14
FWCI
11.24
Type
journal article
Language
en
Open access
hybrid
Licence
pubs.acs.org/page/policy/authorchoice_termsofuse.html (vor, 366 d)
Funders
  • H2020 Industrial Leadership (720776, 814418)
  • Technology Agency of the Czech Republic (TN01000013)
  • Ministerstvo Školství, Mládeže a Telovýchovy (CZ.02.1.01/0.0/0.0/16_013/0001761, CZ.02.1.01/0.0/0.0/16_019/0000868, CZ.02.1.01/0.0/0.0/16_026/0008451, CZ.02.2.69/0.0/0.0/17_050/0008496, LM2015047, LM2015051, LM2015055)
  • European Regional Development Fund (CZ.02.2.69/0.0/0.0/17_050/0008496)

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