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@article{48198,
   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 = {http://dx.doi.org/10.1093/nar/gkaa372},
   keywords = {PROTEIN; SEARCH},
   language = {eng},
   issn = {0305-1048},
   journal = {Nucleic Acids Research},
   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  - 48198
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  - 0305-1048
KW  - PROTEIN
KW  - SEARCH
UR  - 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://dx.doi.org/10.1093/nar/gkaa372.