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Precision enzyme discovery through targeted mining of metagenomic data |
| Shohreh Ariaeenejad1, Javad Gharechahi2, Mehdi Foroozandeh Shahraki3, Fereshteh Fallah Atanaki3, Jian-Lin Han4,5, Xue-Zhi Ding6, Falk Hildebrand7,8, Mohammad Bahram9,10, Kaveh Kavousi3, Ghasem Hosseini Salekdeh11 |
1. Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran;
2. Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran;
3. Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran;
4. Livestock Genetics Program, International Livestock Research, Institute (ILRI), Nairobi, 00100, Kenya;
5. CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China;
6. Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, 730050, China;
7. Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK;
8. Digital Biology, Earlham Institute, Norwich, Norfolk, UK;
9. Department of Ecology, Swedish University of Agricultural Sciences, Ulls Väg 16, 756 51, Uppsala, Sweden;
10. Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St, Tartu, Estonia;
11. Faculty of Natural Sciences, Macquarie University, Sydney, NSW, Australia |
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Abstract Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.
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| Keywords
Metagenomics
Enzyme bioprospecting
Functional-based screening
Sequence-based screening
Protein structure prediction
Natural products
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| Fund:Funding was provided by the Agricultural Biotechnology Research Institute of Iran (ABRII), Swedish Research Council (Vetenskapsrådet grant no.: 2017‐05019), and the BBSRC Institute Strategic Programme Gut Microbes and Health (BB/r012490/1, its constituent project BBS/e/F/000Pr10355). |
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Corresponding Authors:
Kaveh Kavousi,E-mail:kkavousi@ut.ac.ir;Ghasem Hosseini Salekdeh,E-mail:hsalekdeh@yahoo.com
E-mail: kkavousi@ut.ac.ir;hsalekdeh@yahoo.com
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Issue Date: 19 February 2024
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