Machine Learning in Enzyme Engineering

MAZURENKO, Stanislav, Zbyněk PROKOP and Jiří DAMBORSKÝ. Machine Learning in Enzyme Engineering. ACS Catalysis. Washington, D.C.: American Chemical Society, 2020, vol. 10, No 2, p. 1210-1223. ISSN 2155-5435. Available from: https://dx.doi.org/10.1021/acscatal.9b04321.

Basic information

Original name

Machine Learning in Enzyme Engineering

Authors

MAZURENKO, Stanislav (643 Russian Federation, guarantor, belonging to the institution), Zbyněk PROKOP (203 Czech Republic, belonging to the institution) and Jiří DAMBORSKÝ (203 Czech Republic, belonging to the institution)

Edition

ACS Catalysis, Washington, D.C. American Chemical Society, 2020, 2155-5435

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Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10403 Physical chemistry

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 13.084

RIV identification code

RIV/00216224:14310/20:00114835

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1021/acscatal.9b04321

UT WoS

000508466700025

Keywords in English

artificial intelligence; enantioselectivity; function; mechanism; protein engineering; structure-function; solubility; stability

Tags

rivok

Tags

International impact, Reviewed

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Abstract

V originále

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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Links

EF16_013/0001761, research and development project	Name: RECETOX RI
EF17_050/0008496, research and development project	Name: MSCAfellow@MUNI
LM2015047, research and development project	Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ) Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data
LM2015051, research and development project	Name: Centrum pro výzkum toxických látek v prostředí (Acronym: RECETOX RI) Investor: Ministry of Education, Youth and Sports of the CR
LM2015055, research and development project	Name: Centrum pro systémovou biologii (Acronym: C4SYS) Investor: Ministry of Education, Youth and Sports of the CR
TN01000013, research and development project	Name: Personalizovaná medicína - diagnostika a terapie Investor: Technology Agency of the Czech Republic, Personalized Medicine – Diagnostics and Therapy
814418, interní kód MU	Name: Synthetic biology-guided engineering of Pseudomonas putida for biofluorination (Acronym: SinFonia) Investor: European Union, Leadership in enabling and industrial technologies (LEIT) (Industrial Leadership)

Files attached

Mazurenko_Machine_Learning_2020.pdf

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