EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F20%3A00073000" target="_blank" >RIV/00159816:_____/20:00073000 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/20:00117412 RIV/00216305:26230/20:PU138647

Výsledek na webu

<a href="https://academic.oup.com/nar/article/48/W1/W104/5835821" target="_blank" >https://academic.oup.com/nar/article/48/W1/W104/5835821</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/nar/gkaa372" target="_blank" >10.1093/nar/gkaa372</a>

Jazyk výsledku

angličtina

Název v původním jazyce

EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Popis výsledku v původním jazyce

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Despite genomic databases growing exponentially, classical biochemical characterization techniques are time-demanding, cost-ineffective and low-throughput. Therefore, computational methods are being developed to explore the unmapped sequence space efficiently. Selection of putative enzymes for biochemical characterization based on rational and robust analysis of all available sequences remains an unsolved problem. To address this challenge, we have developed EnzymeMiner-a web server for automated screening and annotation of diverse family members that enables selection of hits for wet-lab experiments. EnzymeMiner prioritizes sequences that are more likely to preserve the catalytic activity and are heterologously expressible in a soluble form in Escherichia coli. The solubility prediction employs the in-house SoluProt predictor developed using machine learning. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. The successful use case for the haloalkane dehalogenase family is described in a comprehensive tutorial available on the EnzymeMiner web page.

Název v anglickém jazyce

EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities

Popis výsledku anglicky

Millions of protein sequences are being discovered at an incredible pace, representing an inexhaustible source of biocatalysts. Despite genomic databases growing exponentially, classical biochemical characterization techniques are time-demanding, cost-ineffective and low-throughput. Therefore, computational methods are being developed to explore the unmapped sequence space efficiently. Selection of putative enzymes for biochemical characterization based on rational and robust analysis of all available sequences remains an unsolved problem. To address this challenge, we have developed EnzymeMiner-a web server for automated screening and annotation of diverse family members that enables selection of hits for wet-lab experiments. EnzymeMiner prioritizes sequences that are more likely to preserve the catalytic activity and are heterologously expressible in a soluble form in Escherichia coli. The solubility prediction employs the in-house SoluProt predictor developed using machine learning. EnzymeMiner reduces the time devoted to data gathering, multi-step analysis, sequence prioritization and selection from days to hours. The successful use case for the haloalkane dehalogenase family is described in a comprehensive tutorial available on the EnzymeMiner web page.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

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

Nucleic Acids Research

ISSN

0305-1048

e-ISSN

—

Svazek periodika

48

Číslo periodika v rámci svazku

W1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

"W104"-"W109"

Kód UT WoS článku

000562474100017

EID výsledku v databázi Scopus

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