Precision enzyme discovery through targeted mining of metagenomic data
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...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/152127 |
| _version_ | 1855534565388451840 |
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| author | Ariaeenejad, Shohreh Gharechahi, Javad Foroozandeh Shahraki, Mehdi Fallah Atanaki, Fereshteh Han Jianlin Ding, Xue-Zhi Hildebrand, Falk Bahram, Mohammad Kavousi, Kaveh Hosseini Salekdeh, Ghasem |
| author_browse | Ariaeenejad, Shohreh Bahram, Mohammad Ding, Xue-Zhi Fallah Atanaki, Fereshteh Foroozandeh Shahraki, Mehdi Gharechahi, Javad Han Jianlin Hildebrand, Falk Hosseini Salekdeh, Ghasem Kavousi, Kaveh |
| author_facet | Ariaeenejad, Shohreh Gharechahi, Javad Foroozandeh Shahraki, Mehdi Fallah Atanaki, Fereshteh Han Jianlin Ding, Xue-Zhi Hildebrand, Falk Bahram, Mohammad Kavousi, Kaveh Hosseini Salekdeh, Ghasem |
| author_sort | Ariaeenejad, Shohreh |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace152127 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1521272024-10-30T11:26:35Z Precision enzyme discovery through targeted mining of metagenomic data Ariaeenejad, Shohreh Gharechahi, Javad Foroozandeh Shahraki, Mehdi Fallah Atanaki, Fereshteh Han Jianlin Ding, Xue-Zhi Hildebrand, Falk Bahram, Mohammad Kavousi, Kaveh Hosseini Salekdeh, Ghasem development models data learning demand tools machine learning screening products rumen processes mining analytical methods enzymes properties modeling genetic microorganisms 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. 2024-12 2024-09-11T09:26:01Z 2024-09-11T09:26:01Z Journal Article https://hdl.handle.net/10568/152127 en Open Access Springer Ariaeenejad, S., Gharechahi, J., Foroozandeh Shahraki, M., Fallah Atanaki, F., Han, J.-L., Ding, X.-Z., Hildebrand, F., Bahram, M., Kavousi, K., & Hosseini Salekdeh, G. (2024). Precision enzyme discovery through targeted mining of metagenomic data. Natural Products and Bioprospecting, 14(1). https://doi.org/10.1007/s13659-023-00426-8 |
| spellingShingle | development models data learning demand tools machine learning screening products rumen processes mining analytical methods enzymes properties modeling genetic microorganisms Ariaeenejad, Shohreh Gharechahi, Javad Foroozandeh Shahraki, Mehdi Fallah Atanaki, Fereshteh Han Jianlin Ding, Xue-Zhi Hildebrand, Falk Bahram, Mohammad Kavousi, Kaveh Hosseini Salekdeh, Ghasem Precision enzyme discovery through targeted mining of metagenomic data |
| title | Precision enzyme discovery through targeted mining of metagenomic data |
| title_full | Precision enzyme discovery through targeted mining of metagenomic data |
| title_fullStr | Precision enzyme discovery through targeted mining of metagenomic data |
| title_full_unstemmed | Precision enzyme discovery through targeted mining of metagenomic data |
| title_short | Precision enzyme discovery through targeted mining of metagenomic data |
| title_sort | precision enzyme discovery through targeted mining of metagenomic data |
| topic | development models data learning demand tools machine learning screening products rumen processes mining analytical methods enzymes properties modeling genetic microorganisms |
| url | https://hdl.handle.net/10568/152127 |
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