Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Genome Watch
  • Published:

Machine learning sheds light on microbial dark proteins

Nature Reviews Microbiology volume 22, page 63 (2024)Cite this article

Subjects

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

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

References

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Durairaj, J. et al. Uncovering new families and folds in the natural protein universe. Nature 622, 646–653 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Crysten E. Blaby-Haas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Crysten E. Blaby-Haas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41579-023-01002-0

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing