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

J 2020

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

HON, Jiří; Simeon BORKO; Jan ŠTOURAČ; Zbyněk PROKOP; Jaroslav ZENDULKA et al.

Základní údaje

Originální název

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

Autoři

HON, Jiří; Simeon BORKO; Jan ŠTOURAČ; Zbyněk PROKOP; Jaroslav ZENDULKA; David BEDNÁŘ; Tomas MARTINEK a Jiří DAMBORSKÝ

Vydání

Nucleic acids research, Oxford, Oxford University Press, 2020, 0305-1048

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10608 Biochemistry and molecular biology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 16.971

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/20:00117412

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

PROTEIN; SEARCH

Štítky

143180, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 15. 2. 2023 22:56, Mgr. Michaela Hylsová, Ph.D.

Anotace

V originále

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.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2015047, projekt VaV

Název: Česká národní infrastruktura pro biologická data (Akronym: ELIXIR-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Česká národní infrastruktura pro biologická data

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

814418, interní kód MU

Název: Synthetic biology-guided engineering of Pseudomonas putida for biofluorination (Akronym: SinFonia)

Investor: Evropská unie, Synthetic biology-guided engineering of Pseudomonas putida for biofluorination, Leadership in enabling and industrial technologies (LEIT) (Industrial Leadership)

857560, interní kód MU
(Kód CEP: EF17_043/0009632)

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

Přiložené soubory

1711916.pdf

Citovat

HON, Jiří; Simeon BORKO; Jan ŠTOURAČ; Zbyněk PROKOP; Jaroslav ZENDULKA; David BEDNÁŘ; Tomas MARTINEK a Jiří DAMBORSKÝ. EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities. Nucleic acids research. Oxford: Oxford University Press, 2020, roč. 48, W1, s. "W104"-"W109", 6 s. ISSN 0305-1048. Dostupné z: https://doi.org/10.1093/nar/gkaa372.

@article{1711916,
   author = {Hon, Jiří and Borko, Simeon and Štourač, Jan and Prokop, Zbyněk and Zendulka, Jaroslav and Bednář, David and Martinek, Tomas and Damborský, Jiří},
   article_location = {Oxford},
   article_number = {W1},
   doi = {https://doi.org/10.1093/nar/gkaa372},
   keywords = {PROTEIN; SEARCH},
   language = {eng},
   issn = {0305-1048},
   journal = {Nucleic acids research},
   note = {CZ.02.1.01/0.0/0.0/18_046/0015975 neuplatňovat.},
   title = {EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities},
   url = {https://academic.oup.com/nar/article/48/W1/W104/5835821},
   volume = {48},
   year = {2020}
}

TY  - JOUR
ID  - 1711916
AU  - Hon, Jiří - Borko, Simeon - Štourač, Jan - Prokop, Zbyněk - Zendulka, Jaroslav - Bednář, David - Martinek, Tomas - Damborský, Jiří
PY  - 2020
TI  - EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities
JF  - Nucleic acids research
VL  - 48
IS  - W1
SP  - "W104"-"W109"
EP  - "W104"-"W109"
PB  - Oxford University Press
SN  - 03051048
N1  - CZ.02.1.01/0.0/0.0/18_046/0015975 neuplatňovat.
KW  - PROTEIN
KW  - SEARCH
UR  - https://academic.oup.com/nar/article/48/W1/W104/5835821
L2  - https://academic.oup.com/nar/article/48/W1/W104/5835821
N2  - 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.
ER  -

HON, Jiří; Simeon BORKO; Jan ŠTOURAČ; Zbyněk PROKOP; Jaroslav ZENDULKA; David BEDNÁŘ; Tomas MARTINEK a Jiří DAMBORSKÝ. EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities. \textit{Nucleic acids research}. Oxford: Oxford University Press, 2020, roč.~48, W1, s.~''W104''-''W109'', 6 s. ISSN~0305-1048. Dostupné z: https://doi.org/10.1093/nar/gkaa372.

Přehled o publikaci