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Predicting Function of Class II Diterpene Cyclases in Bacterial Species Using a Sequence Similarity Network
Author(s) -
Lemke Cody,
Nett Ryan
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.766.20
Subject(s) - diterpene , computational biology , biology , natural product , bacteria , function (biology) , biochemistry , genetics
Diterpenoids are natural products commonly derived from plants as well as bacteria and fungi. Many of these secondary metabolites have been shown to possess anti‐inflammatory, antimicrobial, insecticidal, and even antitumor properties making them of great interest in the pharmaceutical and agricultural industries. However, due to their complex structure, metabolic engineering is typically a necessary means of producing these compounds. For this reason, it is important to understand the function and mechanisms of the enzymes that often perform the committed step in diterpenoid biosynthesis, class II diterpene cyclases (DTCs). To better understand the breadth and diversity of bacterial DTCs, a protein sequence similarity network (SSN) was constructed from a previously characterized DTC involved in the synthesis of the plant hormone, gibberellin. This network established a representation of homology between putative DTCs in bacteria that could potentially be used to predict function of uncharacterized genes. Interestingly, this method also demonstrated that some bacterial DTCs have similarity to plant and fungal DTCs, suggesting a possible evolutionary relationship. To assess the SSN results, we cloned and expressed several putative bacterial DTCs in our metabolic engineering system, and in doing so have confirmed their functionality as DTCs. These newly characterized enzymes exhibit a wide range of functionality, and active site analysis suggests that only a few key amino acids may be involved in determining product outcome. Thus, our results demonstrate the efficacy of SSNs for identifying potential DTCs, along with providing insight into the mechanisms underlying the biochemical diversity exhibited by bacterial DTCs. Support or Funding Information Laboratory of Reuben Peters, Iowa State University

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