Skip to main content
Log in

A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The theoretical prediction of the association of a flexible ligand with a protein receptor requires efficient sampling of the conformational space of the ligand. Several docking methodologies are currently available. We propose a new docking technique that performs well at low computational cost. The method uses mutually orthogonal Latin squares to efficiently sample the docking space. A variant of the mean field technique is used to analyze this sample to arrive at the optimum. The method has been previously applied to explore the conformational space of peptides and identify structures with low values for the potential energy. Here we extend this method to simultaneously identify both the low energy conformation as well as a ‘high-scoring’ docking mode. Application of the method to 56 protein–peptide complexes, in which the length of the peptide ligand ranges from three to seven residues, and comparisons with Autodock 3.05, showed that the method works well.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Taylor RD, Jewsbury PJ, Essex JW (2002) J Comput Aided Mol Des 16:151

    Article  CAS  Google Scholar 

  2. Brooijmans N, Kuntz ID (2003) Annu Rev Biophys Biomol Struct 32:335

    Article  CAS  Google Scholar 

  3. Koehl P, Delarue M (1994) J Mol Biol 239:249

    Article  CAS  Google Scholar 

  4. Vengadesan K, Gautham N (2003) Biophys J 84:2897

    CAS  Google Scholar 

  5. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) Nucleic Acids Res 28:235

    Article  CAS  Google Scholar 

  6. Vengadesan K, Gautham N (2004) Biopolymers 74:476

    Article  CAS  Google Scholar 

  7. Vengadesan K, Gautham N (2004) Biochem Biophys Res Commun 316:731

    Article  CAS  Google Scholar 

  8. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Proteins Struct Funct Genet 47:409

    Article  CAS  Google Scholar 

  9. Bursulaya BD, Totrov M, Abagyan R, Brooks CL (2003) J Comput Aided Mol Des 17:755

    Article  CAS  Google Scholar 

  10. Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Fogel LJ, Freer ST (1995) Chem Biol 2:317

    Article  CAS  Google Scholar 

  11. Némethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A, Rumsey S, Scheraga HA (1992) J Phys Chem 96:6472

    Article  Google Scholar 

  12. Wang R, Lu Y, Wang S (2003) J Med Chem 46:2287

    Article  CAS  Google Scholar 

  13. Lengauer T, Rarey M (1996) Curr Opin Struct Biol 6:402

    Article  CAS  Google Scholar 

  14. Hetényi C, van der Spoel D (2002) Protein Sci 11:1729

    Article  CAS  Google Scholar 

  15. Poornima CS, Dean PM (1995) J Comput Aided Mol Des 9:500

    Article  CAS  Google Scholar 

  16. Chung SY, Subbiah S (1996) In: Hunter L, Klein TE, Pac Symp Biocomput. World Scientific, Hawaii, USA, pp 126–141

  17. Mc Donald IK, Thornton JM (1994) J Mol Biol 238:777

    Article  CAS  Google Scholar 

  18. Knegtel RMA, Antoon J, Rullmann C, Boelens R, Kaptein R (1994) J Mol Biol 235:318

    Article  CAS  Google Scholar 

  19. Hubbard SJ, Thornton JM (1993) ‘NACCESS’: computer program. Department of Biochemistry and Molecular Biology, University College, London

  20. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) J Comput Chem 19:1639

    Article  CAS  Google Scholar 

  21. Tame JR, Dodson EJ, Murshudov G, Higgins CF, Wilkinson AJ (1995) Structure 3:1395

    Article  CAS  Google Scholar 

  22. Tame JR, Murshudov G, Dodson EJ, Neil TK, Dodson GG, Higgins CF, Wilkinson AJ (1994) Science 264:1578

    Article  CAS  Google Scholar 

  23. Tame JR, Sleigh SH, Wilkinson AJ, Ladbury JE (1996) Nat Struct Biol 3:998

    Article  CAS  Google Scholar 

  24. Taylor RD, Jewsbury PJ, Essex JW (2002) J Comput Chem 24:1637

    Article  CAS  Google Scholar 

  25. Abagyan RA, Totrov M (1997) J Mol Biol 268:678

    Article  CAS  Google Scholar 

  26. Harel M, Su C-T, Frolow F, Silman I, Sussman JL (1991) Biochemistry 30:5217

    Article  CAS  Google Scholar 

  27. Rosenfeld RJ, Goodsell DS, Musah RA, Morris GM, Goodin DB, Olson AJ (2003) J Comput Aided Mol Des 17:525

    Article  CAS  Google Scholar 

  28. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) J Mol Biol 267:727

    Article  CAS  Google Scholar 

  29. Oberlin D, Scheraga HA (1998) J Comput Chem 19:71

    Article  CAS  Google Scholar 

  30. Baysal C, Atilgan AR (2001) Proteins 45:62

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We thank the Department of Biotechnology, Government of India for financial support under the grant no. BT/PR5476/BID/07/136/2004. We also thank the University Grants Commission, and the Department of Science and Technology, Government of India for support under the CAS program and the FIST program, respectively.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Gautham.

Electronic supplementary material

Fig. 1

A Latin square of order 3. The Latin alphabets A, B, and C are used as symbols for Latin square arrangement. This pattern can be extended to any order, i.e. any number of symbols A, B, C, D (JPG 41 kb)

Fig. 2

Two mutually orthogonal Latin squares (MOLS) of order 3. The Latin alphabets A, B, and C are symbols of first Latin square. The Greek alphabets a, b, and g are symbols of second Latin square, which is orthogonal to the first Latin square (JPG 57 kb)

Fig. 3

Flow chart for the MOLS procedure (JPG 499 kb)

Fig. 4

An example of a set of mutually orthogonal Latin squares, showing three MOLS of order 7, i.e., M = 3, N = 7. Symbols in the first Latin square: a1, a2, a3, a4, a5, a6, a7. Each of these is repeated 7 times to give a total of 49 symbols, which have been arranged in a Latin square. Symbols in second Latin square: b1, b2, b3, b4, b5, b6, b7. The second Latin square is orthogonal to the first. Note that every pairing of a symbol from the first square with one from the second occurs exactly once. Symbols in third Latin square: c1, c2, c3, c4, c5, c6, c7. This is orthogonal to both the other squares. For clarity in this figure, we have used 3 different sets of N symbols. One could use the same set of N symbols and construct N-1 MOLS of order N. One of sub squares of the set of MOLS has been highlighted; its symbols are a7 of the first Latin square, b1 of the second and c5 of the third. In the present application, each symbol within the sub square represents a possible value for the corresponding torsion angle, and each sub square represents a possible conformation of the molecule. The MOLS method requires the potential function to be evaluated at each of these N2 points in the conformation space (JPG 548 kb)

ESM5 (DOC 44 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Arun Prasad, P., Gautham, N. A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling. J Comput Aided Mol Des 22, 815–829 (2008). https://doi.org/10.1007/s10822-008-9216-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-008-9216-5

Keywords

Navigation