Kashif-Khan, Naail;
Savva, Renos;
Frank, Stefanie;
(2024)
Mining metagenomics data for novel bacterial nanocompartments.
NAR Genomics and Bioinformatics
, 6
(1)
, Article lqae025. 10.1093/nargab/lqae025.
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Abstract
Encapsulin nanocompartments are prokaryotic protein-based organelles. T he y displa y div erse natural functions, including mineral storage and stress response. Encapsulins also ha v e applications in synthetic biology, drug deliv ery, v accines, and met abolic engineering . Disco v ering no v el encapsulins is challenging due to inconsistent annotations, and data contamination due to similarity with phage proteins. P re vious studies ha v e disco v ered thousands of encapsulin sequences from bacteria and archaea, but met agenomics dat abases were not specifically interrogated. Metagenomics can provide information on a much larger diversity of unculturable organisms and environmental samples than con v entional sequencing experiments, and metagenomic protein databases ha v e shed light on previously unexplored regions of the protein universe. This study le v erages de v elopments in deep learning for str uct ure and function prediction, to produce a dataset of o v er 1300 no v el putativ e encap- sulin sequences from the MGnify Protein Database. Some well-known encapsulins and their cargo proteins were identified, predominantly pero xidases and ferritin-lik e proteins. A potentially no v el encapsulin-associated biosynthetic gene cluster in v olv ed in producing cytoto xic or an- timicrobial saccharides was discovered using biosynthetic gene cluster prediction. Finally, a cluster of predicted str uct ures with no v el features not seen in experimentally solved encapsulin str uct ures was discovered using large-scale, deep learning-based str uct ure prediction of putative metagenomic encapsulins.
| Type: | Article |
|---|---|
| Title: | Mining metagenomics data for novel bacterial nanocompartments |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/nargab/lqae025 |
| Publisher version: | https://doi.org/10.1093/nargab/lqae025 |
| Language: | English |
| Additional information: | © The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Genetics & Heredity, Mathematical & Computational Biology, PROTEIN-STRUCTURE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10192201 |
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