This month’s Genome Watch highlights the recent use of machine learning to uncover functional ‘dark matter’ in the microbial protein universe.
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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).
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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
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DOI: https://doi.org/10.1038/s41579-023-01002-0