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.

Protein structure homology modeling using SWISS-MODEL workspace

This article has been updated

Abstract

Homology modeling aims to build three-dimensional protein structure models using experimentally determined structures of related family members as templates. SWISS-MODEL workspace is an integrated Web-based modeling expert system. For a given target protein, a library of experimental protein structures is searched to identify suitable templates. On the basis of a sequence alignment between the target protein and the template structure, a three-dimensional model for the target protein is generated. Model quality assessment tools are used to estimate the reliability of the resulting models. Homology modeling is currently the most accurate computational method to generate reliable structural models and is routinely used in many biological applications. Typically, the computational effort for a modeling project is less than 2 h. However, this does not include the time required for visualization and interpretation of the model, which may vary depending on personal experience working with protein structures.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1
Figure 2: Workflow of comparative protein structure modeling using SWISS-MODEL workspace.
Figure 3: Example of a personal user workspace.
Figure 4
Figure 5: Typical view of a SWISS-MODEL workspace result.
Figure 6: Target sequence annotation for the putative protein kinase C delta from Drosophila (PKC δ, UniProt AC: P83099).
Figure 7: Model of the kinase domain of the putative PKC δ from Drosophila shown as ribbon representation in DeepView colored from blue (N terminus) to red (C terminus).

Change history

  • 30 July 2009

    The version of this article initially published indicated that only Torsten Schwede was affiliated with the Swiss Institute of Bioinformatics in addition to the Biozentrum, University of Basel, Basel, Switzerland. However, all six authors are affiliated with both the Biozentrum and the Swiss Institute of Bioinformatics. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1

    Berman, H., Henrick, K., Nakamura, H. & Markley, J.L. The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids. Res. 35, D301–D303 (2007).

    CAS  Article  Google Scholar 

  2. 2

    Wu, C.H. et al. The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids. Res. 34, D187–D191 (2006).

    CAS  Article  Google Scholar 

  3. 3

    Chothia, C. Proteins. One thousand families for the molecular biologist. Nature 357, 543–544 (1992).

    CAS  Article  Google Scholar 

  4. 4

    Chothia, C. & Lesk, A.M. The relation between the divergence of sequence and structure in proteins. EMBO J. 5, 823–826 (1986).

    CAS  Article  Google Scholar 

  5. 5

    Topham, C.M. et al. An assessment of COMPOSER: a rule-based approach to modelling protein structure. Biochem. Soc. Symp. 57, 1–9 (1990).

    CAS  PubMed  Google Scholar 

  6. 6

    Sali, A. & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    CAS  Article  Google Scholar 

  7. 7

    Peitsch, M.C. Protein modelling by e-mail. BioTechnology 13, 658–660 (1995).

    CAS  Google Scholar 

  8. 8

    Tramontano, A. & Morea, V. Assessment of homology-based predictions in CASP5. Proteins 53 (Suppl. 6): 352–368 (2003).

    CAS  Article  Google Scholar 

  9. 9

    Tress, M., Ezkurdia, I., Grana, O., Lopez, G. & Valencia, A. Assessment of predictions submitted for the CASP6 comparative modeling category. Proteins 61 (Suppl. 7): 27–45 (2005).

    CAS  Article  Google Scholar 

  10. 10

    Jauch, R., Yeo, H.C., Kolatkar, P.R. & Clarke, N.D. Assessment of CASP7 structure predictions for template free targets. Proteins 69 (Suppl. 8): 57–67 (2007).

    CAS  Article  Google Scholar 

  11. 11

    Kopp, J., Bordoli, L., Battey, J.N., Kiefer, F. & Schwede, T. Assessment of CASP7 predictions for template-based modeling targets. Proteins 69 (Suppl. 8): 38–56 (2007).

    CAS  Article  Google Scholar 

  12. 12

    Kryshtafovych, A., Fidelis, K. & Moult, J. Progress from CASP6 to CASP7. Proteins 69 (Suppl. 8): 194–207 (2007).

    CAS  Article  Google Scholar 

  13. 13

    Hillisch, A., Pineda, L.F. & Hilgenfeld, R. Utility of homology models in the drug discovery process. Drug Discov. Today 9, 659–669 (2004).

    CAS  Article  Google Scholar 

  14. 14

    Kopp, J. & Schwede, T. Automated protein structure homology modeling: a progress report. Pharmacogenomics 5, 405–416 (2004).

    CAS  Article  Google Scholar 

  15. 15

    Marti-Renom, M.A. et al. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291–325 (2000).

    CAS  Article  Google Scholar 

  16. 16

    Peitsch, M.C. About the use of protein models. Bioinformatics 18, 934–938 (2002).

    CAS  Article  Google Scholar 

  17. 17

    Tramontano, A. In Computational Structural Biology (eds. Schwede T. & Peitsch M.C.) (World Scientific Publishing, Singapore, 2008).

    Google Scholar 

  18. 18

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

    CAS  Article  Google Scholar 

  19. 19

    Soto, C.S., Fasnacht, M., Zhu, J., Forrest, L. & Honig, B. Loop modeling: sampling, filtering, and scoring. Proteins 70, 834–843 (2008).

    CAS  Article  Google Scholar 

  20. 20

    Rohl, C.A., Strauss, C.E., Chivian, D. & Baker, D. Modeling structurally variable regions in homologous proteins with rosetta. Proteins 55, 656–677 (2004).

    CAS  Article  Google Scholar 

  21. 21

    Fiser, A., Do, R.K. & Sali, A. Modeling of loops in protein structures. Protein Sci. 9, 1753–1773 (2000).

    CAS  Article  Google Scholar 

  22. 22

    Canutescu, A.A., Shelenkov, A.A. & Dunbrack, R.L. Jr. A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 12, 2001–2014 (2003).

    CAS  Article  Google Scholar 

  23. 23

    Rost, B. Twilight zone of protein sequence alignments. Protein Eng. 12, 85–94 (1999).

    CAS  Article  Google Scholar 

  24. 24

    Dunbrack, R.L. Jr. Sequence comparison and protein structure prediction. Curr. Opin. Struct. Biol. 16, 374–384 (2006).

    CAS  Article  Google Scholar 

  25. 25

    Sommer, I., Toppo, S., Sander, O., Lengauer, T. & Tosatto, S.C. Improving the quality of protein structure models by selecting from alignment alternatives. BMC Bioinformatics 7, 364 (2006).

    Article  Google Scholar 

  26. 26

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

    CAS  Article  Google Scholar 

  27. 27

    Vingron, M. Near-optimal sequence alignment. Curr. Opin. Struct. Biol. 6, 346–352 (1996).

    CAS  Article  Google Scholar 

  28. 28

    Melo, F. & Feytmans, E. Assessing protein structures with a non-local atomic interaction energy. J. Mol. Biol. 277, 1141–1152 (1998).

    CAS  Article  Google Scholar 

  29. 29

    Sippl, M.J. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213, 859–883 (1990).

    CAS  Article  Google Scholar 

  30. 30

    Zhou, H. & Zhou, Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11, 2714–2726 (2002).

    CAS  Article  Google Scholar 

  31. 31

    Fasnacht, M., Zhu, J. & Honig, B. Local quality assessment in homology models using statistical potentials and support vector machines. Protein Sci. 16, 1557–1568 (2007).

    CAS  Article  Google Scholar 

  32. 32

    Wallner, B. & Elofsson, A. Identification of correct regions in protein models using structural, alignment, and consensus information. Protein Sci. 15, 900–913 (2006).

    CAS  Article  Google Scholar 

  33. 33

    Laskowski, R.A., MacArthur, M.W., Moss, D.S. & Thornton, J.M. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst. 26, 283–291 (1993).

    CAS  Article  Google Scholar 

  34. 34

    Hooft, R.W., Vriend, G., Sander, C. & Abola, E.E. Errors in protein structures. Nature 381, 272 (1996).

    CAS  Article  Google Scholar 

  35. 35

    Aloy, P., Pichaud, M. & Russell, R.B. Protein complexes: structure prediction challenges for the 21st century. Curr. Opin. Struct. Biol. 15, 15–22 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Alber, F. et al. Determining the architectures of macromolecular assemblies. Nature 450, 683–694 (2007).

    CAS  Article  Google Scholar 

  37. 37

    Junne, T., Schwede, T., Goder, V. & Spiess, M. The plug domain of yeast Sec61p is important for efficient protein translocation, but is not essential for cell viability. Mol. Biol. Cell 17, 4063–4068 (2006).

    CAS  Article  Google Scholar 

  38. 38

    Battey, J.N. et al. Automated server predictions in CASP7. Proteins 69 (Suppl. 8): 68–82 (2007).

    CAS  Article  Google Scholar 

  39. 39

    Koh, I.Y. et al. EVA: evaluation of protein structure prediction servers. Nucleic Acids. Res. 31, 3311–3315 (2003).

    CAS  Article  Google Scholar 

  40. 40

    Arnold, K., Bordoli, L., Kopp, J. & Schwede, T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22, 195–201 (2006).

    CAS  Article  Google Scholar 

  41. 41

    Eswar, N. et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids. Res. 31, 3375–3380 (2003).

    CAS  Article  Google Scholar 

  42. 42

    Bates, P.A., Kelley, L.A., MacCallum, R.M. & Sternberg, M.J. Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins (Suppl. 5): 39–46 (2001).

  43. 43

    Fernandez-Fuentes, N., Madrid-Aliste, C.J., Rai, B.K., Fajardo, J.E. & Fiser, A. M4T: a comparative protein structure modeling server. Nucleic Acids Res. 35, W363–W368 (2007).

    Article  Google Scholar 

  44. 44

    Fox, J.A., McMillan, S. & Ouellette, B.F. Conducting research on the web: 2007 update for the bioinformatics links directory. Nucleic Acids Res. 35, W3–W5 (2007).

    Article  Google Scholar 

  45. 45

    Schwede, T., Diemand, A., Guex, N. & Peitsch, M.C. Protein structure computing in the genomic era. Res. Microbiol. 151, 107–112 (2000).

    CAS  Article  Google Scholar 

  46. 46

    Kopp, J. & Schwede, T. The SWISS-MODEL repository of annotated three-dimensional protein structure homology models. Nucleic Acids Res. 32, D230–D234 (2004).

    CAS  Article  Google Scholar 

  47. 47

    Schwede, T., Kopp, J., Guex, N. & Peitsch, M.C. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 31, 3381–3385 (2003).

    CAS  Article  Google Scholar 

  48. 48

    Guex, N. & Peitsch, M.C. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18, 2714–2723 (1997).

    CAS  Article  Google Scholar 

  49. 49

    Andreeva, A. et al. SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res. 32, D226–D229 (2004).

    CAS  Article  Google Scholar 

  50. 50

    Greene, L.H. et al. The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution. Nucleic Acids Res. 35, D291–D297 (2007).

    CAS  Article  Google Scholar 

  51. 51

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

    CAS  Article  Google Scholar 

  52. 52

    Zdobnov, E.M. & Apweiler, R. InterProScan—an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848 (2001).

    CAS  Article  Google Scholar 

  53. 53

    Mulder, N.J. et al. New developments in the InterPro database. Nucleic Acids Res. 35, D224–228 (2007).

    CAS  Article  Google Scholar 

  54. 54

    Jones, D.T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999).

    CAS  Article  Google Scholar 

  55. 55

    Jones, D.T. & Ward, J.J. Prediction of disordered regions in proteins from position specific score matrices. Proteins 53 (Suppl. 6): 573–578 (2003).

    CAS  Article  Google Scholar 

  56. 56

    Jones, D.T., Taylor, W.R. & Thornton, J.M. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33, 3038–3049 (1994).

    CAS  Article  Google Scholar 

  57. 57

    Fink, A.L. Natively unfolded proteins. Curr. Opin. Struct. Biol. 15, 35–41 (2005).

    CAS  Article  Google Scholar 

  58. 58

    Radivojac, P. et al. Intrinsic disorder and functional proteomics. Biophys. J. 92, 1439–1456 (2007).

    CAS  Article  Google Scholar 

  59. 59

    Dyson, H.J. & Wright, P.E. Intrinsically unstructured proteins and their functions. Nat. Rev. Mol. Cell Biol. 6, 197–208 (2005).

    CAS  Article  Google Scholar 

  60. 60

    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).

    CAS  Article  Google Scholar 

  61. 61

    Wheeler, D.L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 33 Database Issue: D39–D45 (2005).

    CAS  Article  Google Scholar 

  62. 62

    Soding, J. Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005).

    Article  Google Scholar 

  63. 63

    Muller, C.W., Schlauderer, G.J., Reinstein, J. & Schulz, G.E. Adenylate kinase motions during catalysis: an energetic counterweight balancing substrate binding. Structure 4, 147–156 (1996).

    CAS  Article  Google Scholar 

  64. 64

    Söding, J., Biegert, A. & Lupas, A.N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244–248 (2005).

    Article  Google Scholar 

  65. 65

    Benkert, P., Tosatto, S.C. & Schomburg, D. QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71, 261–277 (2008).

    CAS  Article  Google Scholar 

  66. 66

    van Gunsteren, W.F. et al. Biomolecular Simulations: the GROMOS96 Manual and User Guide (VdF Hochschulverlag ETHZ, Zürich, 1996).

  67. 67

    Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).

    CAS  Article  Google Scholar 

  68. 68

    Hulo, N. et al. The PROSITE database. Nucleic Acids Res. 34, D227–D230 (2006).

    CAS  Article  Google Scholar 

  69. 69

    Stivers, J.T. & Jiang, Y.L. A mechanistic perspective on the chemistry of DNA repair glycosylases. Chem. Rev. 103, 2729–2759 (2003).

    CAS  Article  Google Scholar 

  70. 70

    Seeberg, E., Eide, L. & Bjoras, M. The base excision repair pathway. Trends Biochem. Sci. 20, 391–397 (1995).

    CAS  Article  Google Scholar 

  71. 71

    Alseth, I. et al. A new protein superfamily includes two novel 3-methyladenine DNA glycosylases from Bacillus cereus, AlkC and AlkD. Mol. Microbiol. 59, 1602–1609 (2006).

    CAS  Article  Google Scholar 

  72. 72

    Groves, M.R. & Barford, D. Topological characteristics of helical repeat proteins. Curr. Opin. Struct. Biol. 9, 383–389 (1999).

    CAS  Article  Google Scholar 

  73. 73

    Dalhus, B. et al. Structural insight into repair of alkylated DNA by a new superfamily of DNA glycosylases comprising HEAT-like repeats. Nucleic Acids Res. 35, 2451–2459 (2007).

    CAS  Article  Google Scholar 

  74. 74

    Nishizuka, Y. Membrane phospholipid degradation and protein kinase C for cell signalling. Neurosci. Res. 15, 3–5 (1992).

    CAS  Article  Google Scholar 

  75. 75

    Mellor, H. & Parker, P.J. The extended protein kinase C superfamily. Biochem. J. 332 (Part 2): 281–292 (1998).

    CAS  Article  Google Scholar 

  76. 76

    Zhang, G., Kazanietz, M.G., Blumberg, P.M. & Hurley, J.H. Crystal structure of the cys2 activator-binding domain of protein kinase C delta in complex with phorbol ester. Cell 81, 917–924 (1995).

    CAS  Article  Google Scholar 

  77. 77

    Henrick, K. & Thornton, J.M. PQS: a protein quaternary structure file server. Trends Biochem. Sci. 23, 358–361 (1998).

    CAS  Article  Google Scholar 

  78. 78

    Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We are grateful to Dr Michael Podvinec for his enthusiastic support and excellent coordination of the Scrum process for the SWISS-MODEL team. We are thankful for financial support of our group by the Swiss Institute of Bioinformatics (SIB).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Torsten Schwede.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bordoli, L., Kiefer, F., Arnold, K. et al. Protein structure homology modeling using SWISS-MODEL workspace. Nat Protoc 4, 1–13 (2009). https://doi.org/10.1038/nprot.2008.197

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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