Investigative mining of sequence data for novel enzymes: A case study with nitrilases

Jennifer L. Seffernick, Sudip K. Samanta, Tai Man Louie, Lawrence P. Wackett, Mani Subramanian

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Mining sequence data is increasingly important for biocatalysis research. However, when relying on sequence data alone, prediction of the reaction catalyzed by a specific protein sequence is often elusive, and substrate specificity is far from trivial. The present study demonstrated an approach of combining sequence data and structures from distant homologs to target identification of new nitrilases that specifically utilize hindered nitrile substrates like mandelonitrile. A total of 212 non-identical target nitrilases were identified from GenBank. Evolutionary trace and sequence clustering methods were used combinatorily to identify a set of nitrilases with presumably distinct substrate specificities. Selected encoding genes were cloned into Escherichia coli. Recombinant E. coli expressing NitA (gi91784632) from Burkholderia xenovorans LB400 was capable of growth on glutaronitrile or adiponitrile as the sole nitrogen source. Purified NitA exhibited highest activity with mandelonitrile, showing a catalytic efficiency (kcat/Km) of 3.6 × 104 M-1 s-1. A second nitrilase predicted from our studies from Bradyrhizobium zaponicum USDA 110 (gi27381513) was likewise shown to prefer mandelonitrile [Zhu, D., Mukherjee, C., Biehl, E.R., Hua, L., 2007. Discovery of a mandelonitrile hydrolase from Bradyrhizobium japonicum USDA110 by rational genome mining. J. Biotechnol. 129 (4), 645-650]. Thus, predictions from sequence analysis and distant superfamily structures yielded enzyme activities with high selectivity for mandelonitrile. These data suggest that similar data mining techniques can be used to identify other substrate-specific enzymes from published, unannotated sequences.

Original languageEnglish (US)
Pages (from-to)17-26
Number of pages10
JournalJournal of Biotechnology
Issue number1
StatePublished - Aug 10 2009

Bibliographical note

Funding Information:
This research was supported by the University of Iowa research funds. We thank Kailin Chew for her assistance during the course of this study, Chi Li Yu for help with HPLC, the Romas Kazlauskas laboratory for the use of their chiral HPLC column, and Jack Richman for help with polarimetry and helpful discussions on chirality.

Copyright 2009 Elsevier B.V., All rights reserved.


  • Genome mining
  • Mandelonitrile
  • Nitrilase


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