Machine Learning in Enzyme Engineering

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00159816:_____/20:00072967

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning in Enzyme Engineering

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning in Enzyme Engineering

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACS Catalysis

ISSN

2155-5435

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1210-1223

Kód UT WoS článku

000508466700025

EID výsledku v databázi Scopus

2-s2.0-85078763420