Publication details

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

Authors

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

Year of publication 2020
Type Article in Periodical
Magazine / Source Nucleic acids research
MU Faculty or unit

Faculty of Science

Citation
Web https://academic.oup.com/nar/article/48/W1/W104/5835821
Doi http://dx.doi.org/10.1093/nar/gkaa372
Keywords PROTEIN; SEARCH
Description 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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