Skip to main content
Log in

Protein structure prediction on the Web: a case study using the Phyre server

  • Protocol
  • Published:

From Nature Protocols

View current issue Submit your manuscript

Abstract

Determining the structure and function of a novel protein is a cornerstone of many aspects of modern biology. Over the past decades, a number of computational tools for structure prediction have been developed. It is critical that the biological community is aware of such tools and is able to interpret their results in an informed way. This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre). New profile–profile matching algorithms have improved structure prediction considerably in recent years. Although the performance of Phyre is typical of many structure prediction systems using such algorithms, all these systems can reliably detect up to twice as many remote homologies as standard sequence-profile searching. Phyre is widely used by the biological community, with >150 submissions per day, and provides a simple interface to results. Phyre takes 30 min to predict the structure of a 250-residue protein.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1: Example of a typical Phyre results page.
Figure 2: Example of pseudomultiple sequence alignment from PSI-Blast.
Figure 3: Example of a typical Phyre alignment view.
Figure 4: Example of predicted functional sites colored by prediction confidence.

Similar content being viewed by others

References

  1. CASP 7 special issue. Proteins 69 (Suppl. 8), 1–207 (2007).

  2. Baker, D. & Sali, A. Protein structure prediction and structural genomics. Science 294, 93–96 (2001).

    Article  CAS  Google Scholar 

  3. Watson, J.D., Laskowski, R.A. & Thornton, J.M. Predicting protein function from sequence and structural data. Curr. Opin. Struct. Biol. 15, 275–284 (2005).

    Article  CAS  Google Scholar 

  4. Qian, B. et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450, 259–264 (2007).

    Article  CAS  Google Scholar 

  5. Rava, P. & Hussain, M.M. Acquisition of triacylglycerol transfer activity by microsomal triglyceride transfer protein during evolution. Biochemistry 46, 12263–12274 (2007).

    Article  CAS  Google Scholar 

  6. Park, H. et al. Discovery of novel alpha-glucosidase inhibitors based on the virtual screening with the homology-modeled protein structure. Bioorg. Med. Chem. 16, 284–292 (2008).

    Article  CAS  Google Scholar 

  7. Altschul, S.F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    Article  CAS  Google Scholar 

  8. Karplus, K., Barrett, C. & Hughey, R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14, 846–856 (1998).

    Article  CAS  Google Scholar 

  9. Ohlson, T., Wallner, B. & Elofsson, A. Profile–profile methods provide improved fold-recognition: a study of different profile–profile alignment methods. Proteins 57, 188–197 (2004).

    Article  CAS  Google Scholar 

  10. Bennett-Lovsey, R.M., Herbert, A.D., Sternberg, M.J.E. & Kelley, L.A. Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins 70, 611–625 (2008).

    Article  CAS  Google Scholar 

  11. Murzin, A.G., Brenner, S.E., Hubbard, T. & Chothia, C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247, 536–540 (1995).

    CAS  PubMed  Google Scholar 

  12. Berman, H.M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  Google Scholar 

  13. McGuffin, L.J., Bryson, K. & Jones, D.T. The PSIPRED protein structure prediction server. Bioinformatics 16, 404–405 (2000).

    Article  CAS  Google Scholar 

  14. Pollastri, G., Przybylski, D., Rost, B. & Baldi, P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47, 228–235 (2002).

    Article  CAS  Google Scholar 

  15. Cole, C., Barber, J.D. & Barton, G.J. The Jpred 3 secondary structure prediction server. Nucleic Acids Res. 36 (Web server issue): W197–W201 (2008).

    Article  CAS  Google Scholar 

  16. Ward, J.J., McGuffin, L.J., Bryson, K., Buxton, B.F. & Jones, D.T. The DISOPRED server for the prediction of protein disorder. Bioinformatics 20, 2138–2139 (2004).

    Article  CAS  Google Scholar 

  17. Tress, M.L., Jones, D.T. & Valenica, A. Predicting reliable regions in protein alignments from sequence profiles. J. Mol. Biol. 330, 705–718 (2003).

    Article  CAS  Google Scholar 

  18. Marchler-Bauer, A. et al. CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res. 35 (Database issue): D237–D240 (2007).

    Article  CAS  Google Scholar 

  19. Finn, R.D. et al. The Pfam protein families database. Nucleic Acids Res. 36 (Database issue): D281–D288 (2008).

    CAS  PubMed  Google Scholar 

  20. Wass, M.N. & Sternberg, M.J.E. ConFunc—functional annotation in the twilight zone. Bioinformatics 24, 798–806 (2008).

    Article  CAS  Google Scholar 

  21. Yao, H. et al. An accurate, sensitive, and scalable method to identify functional sites in protein structures. J. Mol. Biol. 326, 255–261 (2003).

    Article  CAS  Google Scholar 

  22. Kinoshita, K. & Nakamura, H. Identification of protein biochemical functions by similarity search using the molecular surface database eF-site. Protein Sci. 12, 1589–1595 (2003).

    Article  CAS  Google Scholar 

  23. Laskowski, R.A. et al. Protein clefts in molecular recognition and function. Prot. Sci. 5, 2438–2452 (1996).

    CAS  Google Scholar 

  24. Liang, J., Edelsbrunner, H., Fu, P., Sudhakar, P.V. & Subramaniam, S. Analytical shape computation of macromolecules I and II. Proteins 33, 1–17 and 18–29 (1998).

    Article  CAS  Google Scholar 

  25. Jones, D.T. Predicting novel protein folds by using FRAGFOLD. Proteins 45 (Suppl. 5): 127–132 (2001).

    Article  Google Scholar 

  26. Kim, D.E., Chivian, D. & Baker, D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 32 (Web server issue): W526–W531 (2004).

    Article  CAS  Google Scholar 

  27. Zhang, Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69 (Suppl. 8): 108–117 (2007).

    Article  CAS  Google Scholar 

  28. Hulo, N. et al. The 20 years of PROSITE. Nucleic Acids Res. 36 (Database issue): D245–D249 (2008).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

L.A.K. is supported by the BBSRC grant number LDAD PO6300.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lawrence A Kelley.

Ethics declarations

Competing interests

Sternberg is a director and shareholder of Equinox Pharma Ltd. And Dr. Kelley has in the past acted as a consultant for Equinox Pharma Ltd. The Phyre server and code is freely available to academics and is available to commercial users via a license from Equinox Pharma Ltd.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kelley, L., Sternberg, M. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 4, 363–371 (2009). https://doi.org/10.1038/nprot.2009.2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2009.2

  • Springer Nature Limited

This article is cited by

Navigation