eprintid: 10192201 rev_number: 9 eprint_status: archive userid: 699 dir: disk0/10/19/22/01 datestamp: 2024-05-13 11:27:32 lastmod: 2024-05-13 11:27:32 status_changed: 2024-05-13 11:27:32 type: article metadata_visibility: show sword_depositor: 699 creators_name: Kashif-Khan, Naail creators_name: Savva, Renos creators_name: Frank, Stefanie title: Mining metagenomics data for novel bacterial nanocompartments ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F47 keywords: Science & Technology, Life Sciences & Biomedicine, Genetics & Heredity, Mathematical & Computational Biology, PROTEIN-STRUCTURE note: © 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. 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. date: 2024-03-07 date_type: published publisher: OXFORD UNIV PRESS official_url: https://doi.org/10.1093/nargab/lqae025 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2262034 doi: 10.1093/nargab/lqae025 lyricists_name: Frank, Stefanie lyricists_id: SFRAN44 actors_name: Frank, Stefanie actors_id: SFRAN44 actors_role: owner funding_acknowledgements: BB/T008709/1 [Biotechnology and Biological Sciences Research Council]; [London Interdisciplinary Biosciences Consortium Doctoral Training Partnership]; [Oracle for Research]; EP/R013756/1 [EPSRC] full_text_status: public publication: NAR Genomics and Bioinformatics volume: 6 number: 1 article_number: lqae025 pages: 13 issn: 2631-9268 citation: 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 <https://doi.org/10.1093/nargab%2Flqae025>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10192201/1/Frank_lqae025.pdf