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Abstract
The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer–Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putative monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. This work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.
Highlights
The biotechnological potential of marine bacteria has been exploited in several patented technological processes based on marine enzymes [1]
The goal of this work was to identify Baeyer–Villiger and CYP153 Monooxygenases in a metagenomic dataset obtained from shotgun metagenomic sequencing of polar and subpolar coastal sediments [21], and to select sequences presenting promising biotechnological features such as wide substrate range, regio-selectivity or novel specificities
The methodological framework reported in this work allowed the selection of four sequences out of ~14 million protein coding sequences (PCS) as candidates for synthesis and heterologous expression, greatly aiding in the efficient and knowledge-based exploitation of a highly fragmented metagenomic dataset
Summary
The biotechnological potential of marine bacteria has been exploited in several patented technological processes based on marine enzymes [1]. Bioprospecting efforts can be hindered by low-coverage sequence information and inefficient read assembly of shotgun sequenced metagenomes as a result of the high diversity of microbial communities in these environments, in particular in sediments [3] These datasets are comprised mostly of unassembled reads and short scaffolds containing partial protein coding sequences (PCS), with limited biotechnological value. The identification of biocatalyst sequences can be limited by the low coverage of many metagenomic datasets, as several attractive target enzymes are often encoded in low-abundance members of these highly diverse communities [4] These methodological restrictions limit the exploitation of the remarkable amount of biotechnologically relevant genetic resources from yet-to-be cultured microorganisms that are currently stored in public metagenomic databases. The increasing availability of metagenomic data, coupled to improvements in the design and prediction of protein structures will certainly contribute to improving the initialization steps of directed evolution of protein biocatalysts [6]
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