Informace o publikaci

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

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

Rok publikování 2020
Druh Článek v odborném periodiku
Časopis / Zdroj Nucleic acids research
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://academic.oup.com/nar/article/48/W1/W104/5835821
Doi http://dx.doi.org/10.1093/nar/gkaa372
Klíčová slova PROTEIN; SEARCH
Přiložené soubory
Popis 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.
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