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@article{1640721,
author = {Mazurenko, Stanislav and Prokop, Zbyněk and Damborský, Jiří},
article_location = {Washington, D.C.},
article_number = {2},
doi = {http://dx.doi.org/10.1021/acscatal.9b04321},
keywords = {artificial intelligence; enantioselectivity; function; mechanism; protein engineering; structure-function; solubility; stability},
language = {eng},
issn = {2155-5435},
journal = {ACS Catalysis},
title = {Machine Learning in Enzyme Engineering},
url = {https://pubs.acs.org/doi/10.1021/acscatal.9b04321},
volume = {10},
year = {2020}
}
TY - JOUR
ID - 1640721
AU - Mazurenko, Stanislav - Prokop, Zbyněk - Damborský, Jiří
PY - 2020
TI - Machine Learning in Enzyme Engineering
JF - ACS Catalysis
VL - 10
IS - 2
SP - 1210-1223
EP - 1210-1223
PB - American Chemical Society
SN - 21555435
KW - artificial intelligence
KW - enantioselectivity
KW - function
KW - mechanism
KW - protein engineering
KW - structure-function
KW - solubility
KW - stability
UR - https://pubs.acs.org/doi/10.1021/acscatal.9b04321
L2 - https://pubs.acs.org/doi/10.1021/acscatal.9b04321
N2 - 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.
ER -
MAZURENKO, Stanislav, Zbyněk PROKOP a Jiří DAMBORSKÝ. Machine Learning in Enzyme Engineering. \textit{ACS Catalysis}. Washington, D.C.: American Chemical Society, 2020, roč.~10, č.~2, s.~1210-1223. ISSN~2155-5435. Dostupné z: https://dx.doi.org/10.1021/acscatal.9b04321.
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