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.

  • Insight
  • Published:

A potential smoothing algorithm accurately predicts transmembrane helix packing

Nature Structural Biology volume 6, pages 50–55 (1999)Cite this article

An Erratum to this article was published on 01 February 1999

Abstract

Potential smoothing, a deterministic analog of stochastic simulated annealing, is a powerful paradigm for the solution of conformational search problems that require extensive sampling, and should be a useful tool in computational approaches to structure prediction and refinement. A novel potential smoothing and search (PSS) algorithm has been developed and applied to predict the packing of transmembrane helices. The highlight of this method is the efficient manner in which it circumvents the combinatorial explosion associated with the large number of minima on multidimensional potential energy surfaces in order to converge to the global energy minimum. Here we show how our potential smoothing and search method succeeds in finding the global minimum energy structure for the glycophorin A (GpA) transmembrane helix dimer by optimizing interhelical van der Waals interactions over rigid and semi–rigid helices. Structures obtained from our ab initio predictions are in close agreement with recent experimental data.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: One–dimensional schematic of the effect of a smoothing protocol on a potential energy surface.
Figure 2: Schematic of a more realistic potential smoothing protocol for molecular search problems.
Figure 3: Schematic of a helix dimer illustrating the method used to compute helix packing parameters.
Figure 4
Figure 5

References

  1. Popot, J.–L. and Engelman, D.M. Membrane protein folding and oligomerization: The two–stage model. Biochemistry 29, 4031–4037 (1990).

    Article  CAS  Google Scholar 

  2. Kostrowicki, J. and Scheraga, H.A. Some approaches to the multiple–minima problem in protein folding. DIMACS Ser. Discr. Math. & Theor. Comp. Sci. 23, 123– 130 (1996).

    Article  Google Scholar 

  3. Straub, J.E. Optimization techniques with applications to proteins. In Recent developments in theoretical studies of proteins (ed. Elber, R.) 137–196 (World Scientific, Singapore; 1996).

    Chapter  Google Scholar 

  4. Nakamura, S., Hirose, H., Ikeguchi, M. & Doi, J. Conformational energy minimization using a two–stage method. J. Phys. Chem. 99, 8374–8378 (1995).

    Article  CAS  Google Scholar 

  5. Pappu, R.V., Hart, R.K. & Ponder, J.W. Analysis and application of potential energy smoothing and search methods for global optimization. J. Phys. Chem. B 102, 9725–9742 (1998).

    Article  CAS  Google Scholar 

  6. Piela, L., Kostrowicki, J. & Scheraga, H.A. The multiple–minima problem in the conformational analysis of molecules. Deformation of the potential energy hypersurface by the diffusion equation method. J. Phys. Chem. 93, 3339–3346 (1989).

    Article  CAS  Google Scholar 

  7. Kostrowicki, J. & Scheraga, H.A. Application of the diffusion equation method for global optimization to oligopeptides. J. Phys. Chem. 96, 7442– 7449 (1992).

    Article  CAS  Google Scholar 

  8. MacKenzie, K.R., Prestegard, J.H. & Engelman, D.M. A transmembrane helix dimer: Structure and implications. Science 276, 131–133 (1997).

    Article  CAS  Google Scholar 

  9. TINKER Software Tools for Molecular Design, Version 3.6, Washington University School of Medicine, February 1998, available from http://dasher.wustl–edu/tinker/.

  10. Jorgensen, W. L. & Tirado–Rives, J. The OPLS potential functions for proteins. Energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 110, 1657–1666 (1988).

    Article  CAS  Google Scholar 

  11. 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 

  12. Chothia, C., Levitt, M. & Richardson, D. Helix to helix packing in proteins. J. Mol. Biol. 145, 215–250 (1981).

    Article  CAS  Google Scholar 

  13. Liu, L.–P. and Deber, C.M. Guidelines for membrane protein engineering derived from de novo designed model peptides. Biopolymers 47, 41–61 (1998).

    Article  CAS  Google Scholar 

  14. Lemmon, M.A. and Engelman, D.M. Specificity and promiscuity in membrane helix interactions. Quart. Rev. Biophys. 27, 157–218 (1994).

    Article  CAS  Google Scholar 

  15. Brosig, B. and Langosch, D. The dimerization motif of the glycophorin A transmembrane segment in membranes: Importance of glycine residues. Prot. Sci. 7, 1052–1056 (1998).

    Article  CAS  Google Scholar 

  16. Adams, P.D., Engelman, D.M. & Brünger, A.T. Improved prediction for the structure of the dimeric transmembrane domain of glycophorin A obtained through global searching. Proteins Struct. Funct. Genet. 26, 257– 261 (1996).

    Article  CAS  Google Scholar 

  17. Amara, P., Hsu, D. and Straub, J.E. Global energy minimum searches using an approximate solution of the imaginary time Schrödinger equation. J. Phys. Chem. 97, 6715–6721 (1993).

    Article  CAS  Google Scholar 

  18. Kraulis, P. J. MOLSCRIPT: a program to produce both detailed and schematic plots of proteins structures. J. Appl. Crystallogr. 24, 946–950 (1991).

    Article  Google Scholar 

Download references

Acknowledgements

We thank E. Huang for useful discussions. This work was supported by a grant from the DOE Environmental Science Management Program.

Author information

Authors and Affiliations

  1. Center for Molecular Design, Institute for Biomedical Computing, Washington University School of Medicine, St. Louis , 63110, Missouri, USA

    Rohit V. Pappu & Garland R. Marshall

  2. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, 63110, Missouri, USA

    Jay W. Ponder

Authors
  1. Rohit V. Pappu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garland R. Marshall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jay W. Ponder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jay W. Ponder.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pappu, R., Marshall, G. & Ponder, J. A potential smoothing algorithm accurately predicts transmembrane helix packing. Nat Struct Mol Biol 6, 50–55 (1999). https://doi.org/10.1038/4922

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/4922

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