Dataset Information

Diversity of ABBA Prenyltransferases in Marine Streptomyces sp. CNQ-509: Promiscuous Enzymes for the Biosynthesis of Mixed Terpenoid Compounds.

ABSTRACT: Terpenoids are arguably the largest and most diverse family of natural products, featuring prominently in e.g. signalling, self-defence, UV-protection and electron transfer. Prenyltransferases are essential players in terpenoid and hybrid isoprenoid biosynthesis that install isoprene units on target molecules and thereby often modulate their bioactivity. In our search for new prenyltransferase biocatalysts we focused on the marine-derived Streptomyces sp. CNQ-509, a particularly rich source of meroterpenoid chemistry. Sequencing and analysis of the genome of Streptomyces sp. CNQ-509 revealed seven putative phenol/phenazine-specific ABBA prenyltransferases, and one putative indole-specific ABBA prenyltransferase. To elucidate the substrate specificity of the ABBA prenyltransferases and to learn about their role in secondary metabolism, CnqP1 -CnqP8 were produced in Escherichia coli and incubated with various aromatic and isoprenoid substrates. Five of the eight prenyltransferases displayed enzymatic activity. The efficient conversion of dihydroxynaphthalene derivatives by CnqP3 (encoded by AA958_24325) and the co-location of AA958_24325 with genes characteristic for the biosynthesis of THN (tetrahydroxynaphthalene)-derived natural products indicates that the enzyme is involved in the formation of debromomarinone or other naphthoquinone-derived meroterpenoids. Moreover, CnqP3 showed high flexibility towards a range of aromatic and isoprenoid substrates and thus represents an interesting new tool for biocatalytic applications.

SUBMITTER: Leipoldt F

PROVIDER: S-EPMC4684245 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Diversity of ABBA Prenyltransferases in Marine Streptomyces sp. CNQ-509: Promiscuous Enzymes for the Biosynthesis of Mixed Terpenoid Compounds.

Leipoldt Franziska F Zeyhle Philipp P Kulik Andreas A Kalinowski Jörn J Heide Lutz L Kaysser Leonard L

PloS one 20151214 12

Terpenoids are arguably the largest and most diverse family of natural products, featuring prominently in e.g. signalling, self-defence, UV-protection and electron transfer. Prenyltransferases are essential players in terpenoid and hybrid isoprenoid biosynthesis that install isoprene units on target molecules and thereby often modulate their bioactivity. In our search for new prenyltransferase biocatalysts we focused on the marine-derived Streptomyces sp. CNQ-509, a particularly rich source of m ...[more]

PMID: 26659564

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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

Diversity of ABBA Prenyltransferases in Marine Streptomyces sp. CNQ-509: Promiscuous Enzymes for the Biosynthesis of Mixed Terpenoid Compounds.

Publications

Diversity of ABBA Prenyltransferases in Marine Streptomyces sp. CNQ-509: Promiscuous Enzymes for the Biosynthesis of Mixed Terpenoid Compounds.

Similar Datasets

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