Skip to main content
SpringerLink
Log in

Comparison between Generalized-Born and Poisson–Boltzmann methods in physics-based scoring functions for protein structure prediction

  • Original paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

Continuum solvent models such as Generalized-Born and Poisson–Boltzmann methods hold the promise to treat solvation effect efficiently and to enable rapid scoring of protein structures when they are combined with physics-based energy functions. Yet, direct comparison of these two approaches on large protein data set is lacking. Building on our previous work with a scoring function based on a Generalized-Born (GB) solvation model, and short molecular-dynamics simulations, we further extended the scoring function to compare with the MM-PBSA method to treat the solvent effect. We benchmarked this scoring function against seven publicly available decoy sets. We found that, somewhat surprisingly, the results of MM-PBSA approach are comparable to the previous GB-based scoring function. We also discussed the effect to the scoring function accuracy due to presence of large ligands and ions in some native structures of the decoy sets.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cornell WD, Cieplak P, Bayly CI, Goulg IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  2. Jorgensen WL, Tirado-Rives J (1988) J Am Chem Soc 110:1657–1666

    Article  CAS  Google Scholar 

  3. Lazaridis T, Karplus M (1999) Proteins 35:133–152

    Article  PubMed  CAS  Google Scholar 

  4. Lazaridis T, Karplus M (1999) J Mol Biol 288:477–487

    Article  PubMed  CAS  Google Scholar 

  5. Fan ZZ, Hwang J-K, Warshel A (1999) Theor Chem Acc 103:77–80

    CAS  Google Scholar 

  6. Vorobjev YN, Hermans J (1999) Biophys Chem 78:195–205

    Article  PubMed  CAS  Google Scholar 

  7. Feig M, Brooks CL III (2002) Proteins 49:232–245

    Article  PubMed  CAS  Google Scholar 

  8. Dominy B, Brooks C (2002) J Comput Chem 23:147–160

    Article  PubMed  CAS  Google Scholar 

  9. Felts AK, Gallicchio E, Wallqvist A, Levy RM (2002) Proteins 48:404–422

    Article  PubMed  CAS  Google Scholar 

  10. Hsieh M-J, Luo R (2004) Proteins 56:475–486

    Article  PubMed  CAS  Google Scholar 

  11. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) J Comput Chem 24:1999–2012

    Article  PubMed  CAS  Google Scholar 

  12. Lee MC, Duan Y (2004) Proteins 55:620–634

    Article  PubMed  CAS  Google Scholar 

  13. Srinivasan J, Miller J, Kollman PA, Case DA (1998) J Biomol Struct Dyn 16:671–682

    PubMed  CAS  Google Scholar 

  14. Sitkoff D, Sharp KA, Honig B (1998) J Phys Chem 98:1978–1983

    Article  Google Scholar 

  15. Darden TA, York DM, Pedersen LG (1993) J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  16. Reyes CM, Kollman PA (1999) RNA 5:235–244

    Article  PubMed  CAS  Google Scholar 

  17. Cheatham III TE, Kollman PA (1996) J Mol Biol 259:434–444

    Article  PubMed  CAS  Google Scholar 

  18. Kuhn B, Kollman PA (2000) J Med Chem 43:3786–3791

    Article  PubMed  CAS  Google Scholar 

  19. Chong LT, Duan Y, Wang L, Massova I, Kollman PA (1999) Proc Natl Acad Sci USA 96:14330–14335

    Article  PubMed  CAS  Google Scholar 

  20. Park B, Levitt M (1996) J Mol Biol 258:367–392

    Article  PubMed  CAS  Google Scholar 

  21. Lu H, Skolnick J (2001) Prot-Struct Funct Genet 44:223–232

    Article  CAS  Google Scholar 

  22. Lu L, Lu H, Skolnick J (2002) Prot-Struct Funct Genet 49:350–364

    Article  CAS  Google Scholar 

  23. Kihara D, Lu H, Kolinski A, Skolnick J (2001) Proc Natl Acad Sci USA 98:10125–10130

    Article  PubMed  CAS  Google Scholar 

  24. Kihara D, Skolnick J (2003) J Mol Biol 334:793–802

    Article  PubMed  CAS  Google Scholar 

  25. Zhu J, Zhu Q, Shi Y, Liu H (2003) Proteins 52:598–608

    Article  PubMed  CAS  Google Scholar 

  26. Weskamp N, Kuhn D, Hullermeier E, Klebe G (2004) Bioinformatics 20:1522–1526

    Article  PubMed  CAS  Google Scholar 

  27. Bostick DL, Shen M, Vaisman II (2004) Proteins 56:487–501

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful for the decoy sets provided by various groups. This work was supported by research grants from NIH (GM64458 and GM067168 to YD).

Author information

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of Delaware, Newark, , DE, 19716, USA

    Matthew C. Lee & Rong Yang

  2. UC Davis Genome Center and Department of Applied Science, University of California, Davis, CA, 95616, USA

    Yong Duan

Authors
  1. Matthew C. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Duan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, M.C., Yang, R. & Duan, Y. Comparison between Generalized-Born and Poisson–Boltzmann methods in physics-based scoring functions for protein structure prediction. J Mol Model 12, 101–110 (2005). https://doi.org/10.1007/s00894-005-0013-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00894-005-0013-y

Keywords

Search

Navigation