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.

  • Protocol
  • Published:

SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling

Nature Protocols volume 3, pages 1832–1847 (2008)Cite this article

Abstract

SCWRL and MolIDE are software applications for prediction of protein structures. SCWRL is designed specifically for the task of prediction of side-chain conformations given a fixed backbone usually obtained from an experimental structure determined by X-ray crystallography or NMR. SCWRL is a command-line program that typically runs in a few seconds. MolIDE provides a graphical interface for basic comparative (homology) modeling using SCWRL and other programs. MolIDE takes an input target sequence and uses PSI-BLAST to identify and align templates for comparative modeling of the target. The sequence alignment to any template can be manually modified within a graphical window of the target–template alignment and visualization of the alignment on the template structure. MolIDE builds the model of the target structure on the basis of the template backbone, predicted side-chain conformations with SCWRL and a loop-modeling program for insertion–deletion regions with user-selected sequence segments. SCWRL and MolIDE can be obtained at http://dunbrack.fccc.edu/Software.php.

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

Figure 1: Flowchart for homology modeling with MolIDE.
Figure 2
Figure 3: Viewing PSI-BLAST output from the nonredundant sequence database search and secondary structure predictions with PSIPRED within MolIDE.
Figure 4: Viewing and sorting the list of templates.
Figure 5: Manual editing of a target–template sequence alignment.
Figure 6: Image of the model of Bateman domain of human cystathionine beta synthase with S-adenosyl methionine (SAM).

Similar content being viewed by others

References

  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  Google Scholar 

  2. Wang, G. & Dunbrack, R.L. Jr. PISCES: recent improvements to a PDB sequence culling server. Nucleic Acids Res. 33, W94–W98 (2005).

    Article  CAS  Google Scholar 

  3. Lo Conte, L. et al. SCOP: a structural classification of proteins database. Nucleic Acids Res. 28, 257–259 (2000).

    Article  CAS  Google Scholar 

  4. Perutz, M.F., Kendrew, J.C. & Watson, H.C. Structure and function of haemoglobin. J. Mol. Biol. 13, 669–678 (1965).

    Article  CAS  Google Scholar 

  5. Browne, W.J., North, A.C. & Phillips, D.C. A possible three-dimensional structure of bovine alpha-lactalbumin based on that of hen's egg-white lysozyme. J. Mol. Biol. 42, 65–86 (1969).

    Article  CAS  Google Scholar 

  6. Jones, D.T., Taylor, W.R. & Thornton, J.M. A new approach to protein fold recognition. Nature 358, 86–89 (1992).

    Article  CAS  Google Scholar 

  7. Jaroszewski, L., Rychlewski, L., Li, Z., Li, W. & Godzik, A. FFAS03: a server for profile–profile sequence alignments. Nucleic Acids Res. 33, W284–W288 (2005).

    Article  CAS  Google Scholar 

  8. Havel, T.F. & Snow, M.E. A new method for building protein conformations from sequence alignments with homologues of known structure. J. Mol. Biol. 217, 1–7 (1991).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Sanchez, R. & Sali, A. Evaluation of comparative protein structure modeling by MODELLER-3. Proteins (Suppl): 50–58 (1997).

  11. Li, H. et al. Homology modeling using simulated annealing of restrained molecular dynamics and conformational search calculations with CONGEN: application in predicting the three-dimensional structure of murine homeodomain Msx-1. Protein Sci. 6, 956–970 (1997).

    Article  CAS  Google Scholar 

  12. Sahasrabudhe, P.V., Tejero, R., Kitao, S., Furuichi, Y. & Montelione, G.T. Homology modeling of an RNP domain from a human RNA-binding protein: homology-constrained energy optimization provides a criterion for distinguishing potential sequence alignments. Proteins 33, 558–566 (1998).

    Article  CAS  Google Scholar 

  13. Lichtarge, O., Bourne, H.R. & Cohen, F.E. An evolutionary trace method defines binding surfaces common to protein families. J. Mol. Biol. 257, 342–358 (1996).

    Article  CAS  Google Scholar 

  14. Fetrow, J.S., Godzik, A. & Skolnick, J. Functional analysis of the Escherichia coli genome using the sequence-to-structure-to-function paradigm: identification of proteins exhibiting the glutaredoxin/thioredoxin disulfide oxidoreductase activity. J. Mol. Biol. 282, 703–711 (1998).

    Article  CAS  Google Scholar 

  15. Whisstock, J.C. & Lesk, A.M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36, 307–340 (2003).

    Article  CAS  Google Scholar 

  16. Laskowski, R.A., Watson, J.D. & Thornton, J.M. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res. 33, W89–W93 (2005).

    Article  CAS  Google Scholar 

  17. Najmanovich, R.J., Torrance, J.W. & Thornton, J.M. Prediction of protein function from structure: insights from methods for the detection of local structural similarities. Biotechniques 38, 847, 849, 851 (2005).

    Article  CAS  Google Scholar 

  18. 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 

  19. Kim, S.H. et al. Structure-based functional inference in structural genomics. J. Struct. Funct. Genomics 4, 129–135 (2003).

    Article  CAS  Google Scholar 

  20. Zoller, B. & Dahlback, B. Linkage between inherited resistance to activated protein C and factor V gene mutation in venous thrombosis. Lancet 343, 1536–1538 (1994).

    Article  CAS  Google Scholar 

  21. Bogaerts, V. et al. Genetic variability in the mitochondrial serine protease HTRA2 contributes to risk for Parkinson disease. Hum. Mutat. (2008).

  22. Couch, F.J. & Weber, B.L. Mutations and polymorphisms in the familial early-onset breast cancer (BRCA1) gene. Breast Cancer Information Core. Hum. Mutat. 8, 8–18 (1996).

    Article  CAS  Google Scholar 

  23. Karchin, R. et al. LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources. Bioinformatics 21, 2814–2820 (2005).

    Article  CAS  Google Scholar 

  24. Weber, I.T. et al. Molecular modeling of the HIV-1 protease and its substrate binding site. Science 243, 928–931 (1989).

    Article  CAS  Google Scholar 

  25. Weber, I.T. Evaluation of homology modeling of HIV protease. Proteins 7, 172–184 (1990).

    Article  CAS  Google Scholar 

  26. Ring, C.S. et al. Structure-based inhibitor design by using protein models for the development of antiparasitic agents. Proc. Natl. Acad. Sci. USA 90, 3583–3587 (1993).

    Article  CAS  Google Scholar 

  27. 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 

  28. Bower, M., Cohen, F.E. & R.L. Dunbrack, J. SCWRL: a program for building sidechains onto protein backbones (http://www.cmpharm.ucsf.edu/∼bower/scwrl.html, University of California San Francisco, San Francisco, CA, 1997).

  29. 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).

    Article  CAS  Google Scholar 

  30. Wallner, B. & Elofsson, A. All are not equal: a benchmark of different homology modeling programs. Protein Sci. 14, 1315–1327 (2005).

    Article  CAS  Google Scholar 

  31. Canutescu, A.A. & Dunbrack, R.L. Jr. MollDE: a homology modeling framework you can click with. Bioinformatics 21, 2914–2916 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Dunbrack, R.L. Jr. & Karplus, M. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230, 543–574 (1993).

    Article  CAS  Google Scholar 

  34. Dunbrack, R.L. Jr. & Cohen, F.E. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 6, 1661–1681 (1997).

    Article  CAS  Google Scholar 

  35. Dunbrack, R.L. Jr. Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12, 431–440 (2002).

    Article  CAS  Google Scholar 

  36. Lovell, S.C., Word, J.M., Richardson, J.S. & Richardson, D.C. The penultimate rotamer library. Proteins 40, 389–408 (2000).

    Article  CAS  Google Scholar 

  37. Lovell, S.C., Word, J.M., Richardson, J.S. & Richardson, D.C. Asparagine and glutamine rotamers: B-factor cutoff and correction of amide flips yield distinct clustering. Proc. Natl. Acad. Sci. USA 96, 400–405 (1999).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Wheeler, D.L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 36, D13–D21 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  41. Xu, Q., Canutescu, A., Obradovic, Z. & Dunbrack, R.L. Jr. ProtBuD: a database of biological unit structures of protein families and superfamilies. Bioinformatics 22, 2876–2882 (2006).

    Article  CAS  Google Scholar 

  42. Sauder, J.M., Arthur, J.W. & Dunbrack, R.L. Jr. Large-scale comparison of protein sequence alignment algorithms with structure alignments. Proteins 40, 6–22 (2000).

    Article  CAS  Google Scholar 

  43. Xiang, Z., Soto, C.S. & Honig, B. Evaluating conformational free energies: the colony energy and its application to the problem of protein loop prediction. Proc. Natl. Acad. Sci. USA 99, 7432–7437 (2002).

    Article  CAS  Google Scholar 

  44. 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 

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

    Article  CAS  Google Scholar 

  46. Pieper, U. et al. MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 34, D291–D295 (2006).

    Article  CAS  Google Scholar 

  47. Ye, Y. & Godzik, A. FATCAT: a web server for flexible structure comparison and structure similarity searching. Nucleic Acids Res. 32, W582–W585 (2004).

    Article  CAS  Google Scholar 

  48. MacKerell, A.D. Jr. et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B102, 3586–3616 (1998).

    Article  Google Scholar 

  49. Bateman, A. The structure of a domain common to archaebacteria and the homocystinuria disease protein. Trends Biochem. Sci. 22, 12–13 (1997).

    Article  CAS  Google Scholar 

  50. Shan, X., Dunbrack, R.L. Jr., Christopher, S.A. & Kruger, W.D. Mutations in the regulatory domain of cystathionine beta synthase can functionally suppress patient-derived mutations in cis. Hum. Mol. Genet. 10, 635–643 (2001).

    Article  CAS  Google Scholar 

  51. Proudfoot, M. et al. Biochemical and structural characterization of a novel family of cystathionine beta-synthase domain proteins fused to a Zn ribbon-like domain. J. Mol. Biol. 375, 301–315 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH grants R01-HG02302 and R01-GM84453 (to R.L.D.) and P30-CA06927 to Fox Chase Cancer Center. We thank Mark Andrake and Radka Stoyanova for testing MolIDE 1.6.

Author information

Authors and Affiliations

  1. Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, 19111, Pennsylvania, USA

    Qiang Wang, Adrian A Canutescu & Roland L Dunbrack Jr

Authors
  1. Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adrian A Canutescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roland L Dunbrack Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roland L Dunbrack Jr.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Q., Canutescu, A. & Dunbrack, R. SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc 3, 1832–1847 (2008). https://doi.org/10.1038/nprot.2008.184

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2008.184

This article is cited by

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

Advanced 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