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.

  • Comment
  • Published:

Predicting 3D protein structures in light of evolution

Nature Ecology & Evolution volume 5, pages 1195–1198 (2021)Cite this article

Subjects

Recent advances in AI-based 3D protein structure prediction could help address health-related questions, but may also have far-reaching implications for evolution. Here we discuss the advantages and limitations of high-quality 3D structural predictions by AlphaFold2 in unravelling the relationship between protein properties and their impact on fitness, and emphasize the need to integrate in silico structural predictions with functional genomic studies.

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

Fig. 1: AlphaFold2 improves ASR.
Fig. 2: Structure–function relationship in orthologous SAM synthetases.

References

  1. Bershtein, S., Serohijos, A. W. & Shakhnovich, E. I. Curr. Opin. Struct. Biol. 42, 31–40 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Elena, S. F. & Lenski, R. E. Nat. Rev. Genet. 4, 457–469 (2003).

    Article  CAS  Google Scholar 

  3. Fowler, D. M. & Fields, S. Nat. Methods 11, 801–807 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Jasinska, W. et al. Nat. Ecol. Evol. 4, 437–452 (2020).

    Article  PubMed  Google Scholar 

  5. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature https://doi.org/10.1038/s41586-021-03819-2 (2021).

  6. Diss, G. & Lehner, B. eLife 7, e32472 (2018).

    Article  PubMed  Google Scholar 

  7. Schmiedel, J. M. & Lehner, B. Nat. Genet. 51, 1177–1186 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Guan, Y., Dunham, M. J. & Troyanskaya, O. G. Genetics 175, 933–943 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Soria, P. S., McGary, K. L. & Rokas, A. Mol. Biol. Evol. 31, 984–992 (2014).

    Article  CAS  Google Scholar 

  10. Gabaldon, T. & Koonin, E. V. Nat. Rev. Genet. 14, 360–366 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Montelione, G. T. F1000 Biol. Rep. 4, 7 (2012).

    Article  PubMed  Google Scholar 

  12. Laskowski, R. A., Watson, J. D. & Thornton, J. M. J. Mol. Biol. 351, 614–626 (2005).

    Article  CAS  Google Scholar 

  13. Lee, D., Redfern, O. & Orengo, C. Nat. Rev. Mol. Cell Biol. 8, 995–1005 (2007).

    Article  CAS  Google Scholar 

  14. Redfern, O. C., Dessailly, B. H., Dallman, T. J., Sillitoe, I. & Orengo, C. A. PLoS Comput. Biol. 5, e1000485 (2009).

    Article  PubMed  Google Scholar 

  15. Harms, M. J. & Thornton, J. W. Curr. Opin. Struct. Biol. 20, 360–366 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Levin, L. & Mishmar, D. Nat. Ecol. Evol. 1, 41 (2017).

    Article  Google Scholar 

  17. Aadland, K. & Kolaczkowski, B. Biol. Evol. 12, 1549–1565 (2020).

    CAS  Google Scholar 

  18. Kleiner, D. et al. J. Mol. Biol. 431, 4796–4816 (2019).

    Article  CAS  Google Scholar 

  19. Boehr, D. D., Nussinov, R. & Wright, P. E. Nat. Chem. Biol. 5, 789–796 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Bershtein, S. et al. PLoS Genet. 11, e1005612 (2015).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  22. Gershoni, M. et al. J. Mol. Biol. 404, 158–171 (2010).

    Article  CAS  Google Scholar 

  23. Sutto, L., Marsili, S., Valencia, A. & Gervasio, F. L. Proc. Natl Acad. Sci. USA 112, 13567–13572 (2015).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

D.M. is funded by the Israel Science Foundation (ISF) grant 372/17 and S.B. is funded by an ISF grant 1630/15.

Author information

Authors and Affiliations

  1. Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Shimon Bershtein, Daniel Kleiner & Dan Mishmar

Authors
  1. Shimon Bershtein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Kleiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Mishmar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shimon Bershtein or Dan Mishmar.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bershtein, S., Kleiner, D. & Mishmar, D. Predicting 3D protein structures in light of evolution. Nat Ecol Evol 5, 1195–1198 (2021). https://doi.org/10.1038/s41559-021-01519-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01519-8

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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