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Machine learning sheds light on microbial dark proteins

  • Genome Watch
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From Nature Reviews Microbiology

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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

  1. Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).

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  2. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

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  3. Barrio-Hernandez, I. et al. Clustering predicted structures at the scale of the known protein universe. Nature 622, 637–645 (2023).

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Authors and Affiliations

  1. Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Aeron Tynes Hammack & Crysten E. Blaby-Haas

  2. DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Crysten E. Blaby-Haas

Authors
  1. Aeron Tynes Hammack
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  2. Crysten E. Blaby-Haas
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Correspondence to Crysten E. Blaby-Haas.

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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

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