Protein language models uncover carbohydrate-active enzyme function in metagenomics
Open accessPublished version of a preprint
Abstract
Background: The functional annotation of uncharacterized microbial enzymes from metagenomic data remains a significant challenge, limiting our understanding of microbial metabolic dynamics. Traditional annotation methods often rely on sequence homology, which can fail to identify remote homologs or enzymes with structural rather than sequence conservation. To address this gap, we developed CAZyLingua, the first annotation tool to use protein language models (pLMs) for the accurate classification of carbohydrate-active enzyme (CAZyme) families and subfamilies. Results: CAZyLingua demonstrated high performance, maintaining precision and recall comparable to state-of-the-art hidden Markov model-based methods while outperforming purely sequence-based approaches. When applied to a metagenomic gene catalog from mother/infant pairs, CAZyLingua identified over 27,000 putative CAZymes missed by other tools, including horizontally-transferred enzymes implicated in infant microbiome development. In datasets from patients with Crohn’s disease and IgG4-related disease, CAZyLinuga uncovered disease-associated CAZymes, highlighting an expansion of carbohydrate esterases (CEs) in IgG4-related disease. A CE17 enzyme predicted to be overabundant in Crohn’s disease was functionally validated, confirming its catalytic activity on acetylated manno-oligosaccharides. Conclusions: CAZyLingua is a powerful tool that effectively augments existing functional annotation pipelines for CAZymes. By leveraging the deep contextual information captured by pLMs, our method can uncover novel CAZyme diversity and reveal enzymatic functions relevant to health and disease, contributing to a further understanding of biological processes related to host health and nutrition.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Multivariable association discovery in population-scale meta-omics studies
Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E. H., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009442 · Cited by 2,596 · see citing works
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · cited 1× · see citing works
“4) [37, 42] to generate embeddings for each of these sequences using a V100 GPU.”
Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078 · see citing works
Charbonneau, M. R., O’Donnell, D., Blanton, L. V., Totten, S. M., Davis, J. C. C., Barratt, M. J., Cheng, J., Guruge, J., Talcott, M., Bain, J. R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.01.024 · Cited by 606 · see citing works
Carbohydrate-active enzymes (CAZymes) in the gut microbiome
Wardman, J. F., Bains, R. K., Rahfeld, P., & Withers, S. G. (2022) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00712-1 · Cited by 597 · cited 1× · see citing works
“Metagenomic sequencing and functional ‘omics have shown that CAZymes support the growth of beneficial microbes in infants by catabolizing human milk oligosaccharides (HMOs) [17, 18].”
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483 · cited 1× · see citing works
“…models (pLMs), such as those used for structural prediction and other tasks, demonstrate remarkable capabilities in decoding the intricate amino acid language of proteins, which facilitates their functional annotation through a distinct approach compared to sequence-based alignment methods [34–38].”
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
Using deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01179-w · Cited by 332 · see citing works
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- Authors
- Thurimella, K., Mohamed, A. M. T., Li, C., Vatanen, T., Graham, D. B., Owens, R. M., La Rosa, S. L., Plichta, D. R., Bacallado, S., & Xavier, R. J.
- Affiliations
- Broad Institute (US)
- University of Cambridge (GB)
- Corresponding author
- Plichta, Damian R.
- Bacallado, Sergio
- Xavier, Ramnik J.
- ORCID
- Venue
- BMC Bioinformatics
- Published
- 2025-11-26
- Volume
- 26
- Issue
- 1
- Pages
- 285-285
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W4416715651
- Cited by
- 9
- References
- 13
- Citations by year
- 2026: 9
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- Gates Cambridge Trust
- Rotary Foundation
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Enzyme Production and Characterization
- Keywords
- protein language models, carbohydrate-active enzymes, metagenomics, carbohydrate esterases, infant microbiome, Crohn's disease, IgG4-related disease, manno-oligosaccharides, horizontal gene transfer, functional annotation
- MeSH terms
- Enzymes, Humans, Carbohydrate Metabolism, Metagenomics, Molecular Sequence Annotation
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