This month’s Genome Watch highlights the recent use of machine learning to uncover functional ‘dark matter’ in the microbial protein universe.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Barrio-Hernandez, I. et al. Clustering predicted structures at the scale of the known protein universe. Nature 622, 637–645 (2023).
Durairaj, J. et al. Uncovering new families and folds in the natural protein universe. Nature 622, 646–653 (2023).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hammack, A.T., Blaby-Haas, C.E. Machine learning sheds light on microbial dark proteins. Nat Rev Microbiol 22, 63 (2024). https://doi.org/10.1038/s41579-023-01002-0
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41579-023-01002-0