The evolution of genome mining in microbes – a review

This article reviews the development of genome mining strategies in bacteria during the last decade.</p

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.</p

Genome mining in Amycolatopsis balhimycina for ferredoxins capable of supporting cytochrome P450 enzymes involved in glycopeptide antibiotic biosynthesis

Ferredoxins are required to supply electrons to the cytochrome P450 enzymes involved in cross-linking reactions during the biosynthesis of the glycopeptide antibiotics balhimycin and vancomycin. However, the biosynthetic gene clusters for these antibiotics contain no ferredoxin- or ferredoxin reductase-like genes. In a search for potential ferredoxin partners for these P450s, here, we report an in silico analysis of the draft genome sequence of the balhimycin producer Amycolatopsis balhimycina, which revealed 11 putative Fe-S-containing ferredoxin genes. We show that two members (balFd-V and balFd-VII), produced as native-like holo-[3Fe-4S] ferredoxins in Escherichia coli, could supply electrons to the P450 OxyB (CYP165B) from both A. balhimycina and the vancomycin producer Amycolatopsis orientalis, and support in vitro turnover of peptidyl carrier protein-bound peptide substrates into monocyclic cross-linked products. These results show that ferredoxins encoded in the antibiotic-producing strain can act in a degenerate manner in supporting the catalytic functions of glycopeptide biosynthetic P450 enzymes from the same as well as heterologous gene cluster

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies

NRPSpredictor2-a web server for predicting NRPS adenylation domain specificity

The products of many bacterial non-ribosomal peptide synthetases (NRPS) are highly important secondary metabolites, including vancomycin and other antibiotics. The ability to predict substrate specificity of newly detected NRPS Adenylation (A-) domains by genome sequencing efforts is of great importance to identify and annotate new gene clusters that produce secondary metabolites. Prediction of A-domain specificity based on the sequence alone can be achieved through sequence signatures or, more accurately, through machine learning methods. We present an improved predictor, based on previous work (NRPSpredictor), that predicts A-domain specificity using Support Vector Machines on four hierarchical levels, ranging from gross physicochemical properties of an A-domain's substrates down to single amino acid substrates. The three more general levels are predicted with an F-measure better than 0.89 and the most detailed level with an average F-measure of 0.80. We also modeled the applicability domain of our predictor to estimate for new A-domains whether they lie in the applicability domain. Finally, since there are also NRPS that play an important role in natural products chemistry of fungi, such as peptaibols and cephalosporins, we added a predictor for fungal A-domains, which predicts gross physicochemical properties with an F-measure of 0.84. The service is available at http://nrps.informatik.uni-tuebingen.de/