Identification of Sare0718 As an Alanine-Activating Adenylation Domain in Marine Actinomycete Salinispora arenicola CNS-205Report as inadecuate

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Amino acid adenylation domains A domains are critical enzymes that dictate the identity of the amino acid building blocks to be incorporated during nonribosomal peptide NRP biosynthesis. NRPs represent a large group of valuable natural products that are widely applied in medicine, agriculture, and biochemical research. Salinispora arenicola CNS-205 is a representative strain of the first discovered obligate marine actinomycete genus, whose genome harbors a large number of cryptic secondary metabolite gene clusters.

Methodology-Principal Findings

In order to investigate cryptic NRP-related metabolites in S. arenicola CNS-205, we cloned and identified the putative gene sare0718 annotated -amino acid adenylation domain-. Firstly, the general features and possible functions of sare0718 were predicted by bioinformatics analysis, which suggested that Sare0718 is a soluble protein with an AMP-binding domain contained in the sequence and its cognate substrate is L-Val. Then, a GST-tagged fusion protein was expressed and purified to further explore the exact adenylation activity of Sare0718 in vitro. By a newly mentioned nonradioactive malachite green colorimetric assay, we found that L-Ala but not L-Val is the actual activated amino acid substrate and the basic kinetic parameters of Sare0718 for it are Km = 0.1164±0.0159 mM, Vmax = 3.1484±0.1278 µM-min, kcat = 12.5936±0.5112 min−1.


By revealing the biochemical role of sare0718 gene, we identified an alanine-activating adenylation domain in marine actinomycete Salinispora arenicola CNS-205, which would provide useful information for next isolation and function elucidation of the whole cryptic nonribosomal peptide synthetase NRPS-related gene cluster covering Sare0718. And meanwhile, this work also enriched the biochemical data of A domain substrate specificity in newly discovered marine actinomycete NRPS system, which bioinformatics prediction will largely depend on.

Author: Sisi Xia, Yanlin Ma, Wei Zhang, Yi Yang, Shaowen Wu, Minzhe Zhu, Lingfu Deng, Bing Li, Zhonglai Liu , Chao Qi



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