Machine Learning in Enzyme Engineering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F20%3A00114835" target="_blank" >RIV/00216224:14310/20:00114835 - isvavai.cz</a>
Alternative codes found
RIV/00159816:_____/20:00072967
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acscatal.9b04321" target="_blank" >https://pubs.acs.org/doi/10.1021/acscatal.9b04321</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acscatal.9b04321" target="_blank" >10.1021/acscatal.9b04321</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning in Enzyme Engineering
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACS Catalysis
ISSN
2155-5435
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1210-1223
UT code for WoS article
000508466700025
EID of the result in the Scopus database
2-s2.0-85078763420