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.

  • NEWS AND VIEWS

A watershed moment for protein structure prediction

Two threads of research in the quest for methods that predict the 3D structures of proteins from their amino-acid sequences have become fully intertwined. The result is a leap forward in the accuracy of predictions.
  1. Mohammed AlQuraishi

    1. Mohammed AlQuraishi is in the Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.

    View author publications

    You can also search for this author in PubMed  Google Scholar

Proteins perform or catalyse nearly all chemical and mechanical processes in cells. Synthesized as linear chains of amino-acid residues, most proteins spontaneously fold into one or a small number of favoured three-dimensional structures. The sequence of amino acids specifies a protein’s structure and range of motion, which in turn determine its function. Over decades, structural biologists have experimentally determined thousands of protein structures, but the difficulty of these studies has made the promise of a computational approach for predicting protein structure from sequence alluring. Writing in Nature, Senior et al.1 describe an algorithm, AlphaFold, that takes a leap forward in solving this classic problem by bringing to bear modern machine-learning techniques.

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 577, 627-628 (2020)

doi: https://doi.org/10.1038/d41586-019-03951-0

References

  1. Senior, A. W. et al. Nature 577, 706–710 (2020).

    Article  Google Scholar 

  2. Guvench, O. & MacKerell, A. D. Jr Methods Mol. Biol. 443, 63–88 (2008).

    Article  PubMed  Google Scholar 

  3. Maximova, T., Moffatt, R., Ma, B., Nussinov, R. & Shehu, A. PLoS Comput. Biol. 12, e1004619 (2016).

    Article  PubMed  Google Scholar 

  4. Bryngelson, J. D., Onuchic, J. N., Socci, N. D. & Wolynes, P. G. Proteins 21, 167–195 (1995).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  6. Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. Bioinformatics 28, 184–190 (2012).

    Article  PubMed  Google Scholar 

  7. Kamisetty, H., Ovchinnikov, S. & Baker, D. Proc. Natl Acad. Sci. USA 110, 15674–15679 (2013).

    Article  PubMed  Google Scholar 

  8. Lapedes, A. S., Giraud, B. G., Liu, L. & Stormo, G. D. IMS Lecture Notes Monogr. Ser. 33, 236–256 (1999).

    Article  Google Scholar 

  9. Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. PLoS Comput. Biol. 13, e1005324 (2017).

    Article  PubMed  Google Scholar 

  10. Hills, R. D. & Brooks, C. L. Int. J. Mol. Sci. 10, 889–905 (2009).

    Article  PubMed  Google Scholar 

  11. AlQuraishi, M. Cell Syst. 8, 292–301 (2019).

    Article  PubMed  Google Scholar 

  12. Ingraham, J., Riesselman, A., Sander, C. & Marks, D. in 7th Int. Conf. Learn. Represent. https://openreview.net/forum?id=Byg3y3C9Km (2019).

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