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Title: Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach

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. As a result, this work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable startingmore » point for further developments.« less

Authors:
 [1];  [1];  [2];  [3];  [4];  [5];  [6]; ORCiD logo [1]
  1. CONICET, Chubut (Argentina)
  2. CONICET, Rosario (Argentina)
  3. Instituto Antartico Argentino, Ciudad Autonoma de Buenos Aires (Argentina); CONICET - Univ. de Buenos Aires, Ciudad Autonoma de Buenos Aires (Argentina)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. Sodertorn, Univ., Huddinge (Sweden)
  6. Fram - High North Research Centre for Climate and the Environment, Tromso (Norway); UiT the Artic Univ. of Norway, Tromso (Norway)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1420452
Alternate Identifier(s):
OSTI ID: 1356489
Report Number(s):
PNNL-SA-124674
Journal ID: ISSN 1660-3397; MDARE6
Grant/Contract Number:  
AC05-76RL01830; AC05-76RLO1830
Resource Type:
Published Article
Journal Name:
Marine Drugs
Additional Journal Information:
Journal Volume: 15; Journal Issue: 4; Journal ID: ISSN 1660-3397
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; bacterial cyochrome P450; Baeyer-Villiger Monooxygenases; Bioprospecting; biocatalysts; phylogenetic; analysis; molecular modeling

Citation Formats

Musumeci, Matias A., Lozada, Mariana, Rial, Daniela V., Mac Cormack, Walter P., Jansson, Janet K., Sjoling, Sara, Carroll, JoLynn, and Dionisi, Hebe M. Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach. United States: N. p., 2017. Web. doi:10.3390/md15040114.
Musumeci, Matias A., Lozada, Mariana, Rial, Daniela V., Mac Cormack, Walter P., Jansson, Janet K., Sjoling, Sara, Carroll, JoLynn, & Dionisi, Hebe M. Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach. United States. doi:10.3390/md15040114.
Musumeci, Matias A., Lozada, Mariana, Rial, Daniela V., Mac Cormack, Walter P., Jansson, Janet K., Sjoling, Sara, Carroll, JoLynn, and Dionisi, Hebe M. Sun . "Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach". United States. doi:10.3390/md15040114.
@article{osti_1420452,
title = {Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach},
author = {Musumeci, Matias A. and Lozada, Mariana and Rial, Daniela V. and Mac Cormack, Walter P. and Jansson, Janet K. and Sjoling, Sara and Carroll, JoLynn and Dionisi, Hebe M.},
abstractNote = {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. As a result, this work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.},
doi = {10.3390/md15040114},
journal = {Marine Drugs},
number = 4,
volume = 15,
place = {United States},
year = {2017},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3390/md15040114

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SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling
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