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.

  • TECHNOLOGY FEATURE

Artificial intelligence powers protein-folding predictions

Deep-learning algorithms such as AlphaFold2 and RoseTTAFold can now predict a protein’s 3D shape from its linear sequence — a huge boon to structural biologists.
  1. Michael Eisenstein

    1. Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

    View author publications

    You can also search for this author in PubMed  Google Scholar

Three dimensional structure of the ring-shaped human nuclear pore complex modelled using AlphaFold

A model of the human nuclear pore complex, built using AlphaFold2 and structural data. Credit: Agnieszka Obarska-Kosinska

Rarely does scientific software spark such sensational headlines. “One of biology’s biggest mysteries ‘largely solved’ by AI”, declared the BBC. Forbes called it “the most important achievement in AI — ever”. The buzz over the November 2020 debut of AlphaFold2, Google DeepMind’s artificial-intelligence (AI) system for predicting the 3D structure of proteins, has only intensified since the tool was made freely available in July.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Nature 599, 706-708 (2021)

doi: https://doi.org/10.1038/d41586-021-03499-y

References

  1. Porta-Pardo, E., Ruiz-Serra, V. & Valencia, A. Preprint at bioRxiv https://doi.org/10.1101/2021.08.03.454980 (2021).

  2. Jumper, J. et al. Nature 596, 583–589 (2021).

    Article  PubMed  Google Scholar 

  3. Tunyasuvunakool, K. et al. Nature 596, 590–596 (2021).

    Article  PubMed  Google Scholar 

  4. Zheng, W. et al. Proteins https://doi.org/10.1002/prot.26193 (2021).

    Article  Google Scholar 

  5. Yang, J. et al. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020).

    Article  PubMed  Google Scholar 

  6. Xu, J. Proc. Natl. Acad. Sci. USA 116, 16856–16865 (2019).

    Article  PubMed  Google Scholar 

  7. Marks, D. S. et al. PLoS ONE 6, e28766 (2011).

    Article  PubMed  Google Scholar 

  8. Baek, M. et al. Science 373, 871–876 (2021).

    Article  PubMed  Google Scholar 

  9. Mirdita, M. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.08.15.456425 (2021).

  10. Bryant, P., Pozzati, G. & Elofsson, A. Preprint at bioRxiv https://doi.org/10.1101/2021.09.15.460468 (2021).

  11. Humphreys, I. R. et al. Science https://doi.org/10.1126/science.abm4805 (2021).

    Article  Google Scholar 

  12. Evans, R. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2021).

  13. Akdel, M. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.09.26.461876 (2021).

Download references

Subjects

Latest on:

Nature Careers

Jobs

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

Search

Advanced search

Quick links