Detail publikace

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

HON, J. BORKO, S. ŠTOURAČ, J. PROKOP, Z. BEDNÁŘ, D. ZENDULKA, J. MARTÍNEK, T. DAMBORSKÝ, J.

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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. EnzymeMiner is a universal tool applicable to any enzyme family that provides an interactive and easy-to-use web interface freely available at https://loschmidt.chemi.muni.cz/enzymeminer/.

Anglický abstrakt

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. EnzymeMiner is a universal tool applicable to any enzyme family that provides an interactive and easy-to-use web interface freely available at https://loschmidt.chemi.muni.cz/enzymeminer/.

Dokumenty

BibTex


@article{BUT168156,
  author="Jiří {Hon} and Simeon {Borko} and Jan {Štourač} and Zbyněk {Prokop} and David {Bednář} and Jaroslav {Zendulka} and Tomáš {Martínek} and Jiří {Damborský}",
  title="EnzymeMiner: automated mining of soluble enzymes with diverse structures, catalytic properties and stabilities",
  annote="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. EnzymeMiner is a universal tool applicable
to any enzyme family that provides an interactive and easy-to-use web interface
freely available at https://loschmidt.chemi.muni.cz/enzymeminer/.",
  address="NEUVEDEN",
  chapter="168156",
  doi="10.1093/nar/gkaa372",
  edition="NEUVEDEN",
  howpublished="online",
  institution="NEUVEDEN",
  number="1",
  volume="48",
  year="2020",
  month="june",
  pages="104--109",
  publisher="NEUVEDEN",
  type="journal article in Web of Science"
}