Annotating the microbial dark matter with HiFi-NN
Summary: The accurate computational annotation of protein sequences with enzymatic function remains a fundamental challenge in bioinformatics. Here, we present HiFi-NN (Hierarchically-Finetuned Nearest Neighbor search) which annotates protein sequences to the 4th level of Enzyme Commission (EC) numb...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225007412 |
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| author | Gavin Ayres Geraldene Munsamy Michael Heinzinger Noelia Ferruz Kevin Yang Bastiaan Bergman Philipp Lorenz |
| author_facet | Gavin Ayres Geraldene Munsamy Michael Heinzinger Noelia Ferruz Kevin Yang Bastiaan Bergman Philipp Lorenz |
| author_sort | Gavin Ayres |
| collection | DOAJ |
| description | Summary: The accurate computational annotation of protein sequences with enzymatic function remains a fundamental challenge in bioinformatics. Here, we present HiFi-NN (Hierarchically-Finetuned Nearest Neighbor search) which annotates protein sequences to the 4th level of Enzyme Commission (EC) number with greater precision and recall than state-of-the-art deep learning methods. Furthermore, we show that this method can correctly identify the EC number of a given sequence to lower identities than BLASTp. We show that performance can be improved by increasing the diversity of the lookup set in both sequence space and the environment the sequence has been sampled from. We proceed to show that we can correct specific mis-annotations in the BRENDA enzymes database reproducing results found by others. Finally, we use HiFi-NN to annotate functional dark-matter protein sequences from NMPFamDB. Our findings pave the way for more accurate functional annotation in silico, especially for proteins from distant sequence space. |
| format | Article |
| id | doaj-art-28d38f991adb4c9e88de9c009b7bfa88 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-28d38f991adb4c9e88de9c009b7bfa882025-08-20T01:55:37ZengElsevieriScience2589-00422025-06-0128611248010.1016/j.isci.2025.112480Annotating the microbial dark matter with HiFi-NNGavin Ayres0Geraldene Munsamy1Michael Heinzinger2Noelia Ferruz3Kevin Yang4Bastiaan Bergman5Philipp Lorenz6Basecamp Research Ltd., London, UK; Corresponding authorBasecamp Research Ltd., London, UKSchool of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics & Computational Biology, TUM (Technical University of Munich), Munich, GermanyCentre for Genomic Regulation, Barcelona, SpainMicrosoft Research New England, Cambridge, MA, USABasecamp Research Ltd., London, UKBasecamp Research Ltd., London, UKSummary: The accurate computational annotation of protein sequences with enzymatic function remains a fundamental challenge in bioinformatics. Here, we present HiFi-NN (Hierarchically-Finetuned Nearest Neighbor search) which annotates protein sequences to the 4th level of Enzyme Commission (EC) number with greater precision and recall than state-of-the-art deep learning methods. Furthermore, we show that this method can correctly identify the EC number of a given sequence to lower identities than BLASTp. We show that performance can be improved by increasing the diversity of the lookup set in both sequence space and the environment the sequence has been sampled from. We proceed to show that we can correct specific mis-annotations in the BRENDA enzymes database reproducing results found by others. Finally, we use HiFi-NN to annotate functional dark-matter protein sequences from NMPFamDB. Our findings pave the way for more accurate functional annotation in silico, especially for proteins from distant sequence space.http://www.sciencedirect.com/science/article/pii/S2589004225007412MicrobiologyComputer science |
| spellingShingle | Gavin Ayres Geraldene Munsamy Michael Heinzinger Noelia Ferruz Kevin Yang Bastiaan Bergman Philipp Lorenz Annotating the microbial dark matter with HiFi-NN iScience Microbiology Computer science |
| title | Annotating the microbial dark matter with HiFi-NN |
| title_full | Annotating the microbial dark matter with HiFi-NN |
| title_fullStr | Annotating the microbial dark matter with HiFi-NN |
| title_full_unstemmed | Annotating the microbial dark matter with HiFi-NN |
| title_short | Annotating the microbial dark matter with HiFi-NN |
| title_sort | annotating the microbial dark matter with hifi nn |
| topic | Microbiology Computer science |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225007412 |
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