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. 2000 Apr;9(4):765–775. doi: 10.1110/ps.9.4.765

Analysis of knowledge-based protein-ligand potentials using a self-consistent method.

J Shimada 1, A V Ishchenko 1, E I Shakhnovich 1
PMCID: PMC2144609  PMID: 10794420

Abstract

We propose a self-consistent approach to analyze knowledge-based atom-atom potentials used to calculate protein-ligand binding energies. Ligands complexed to actual protein structures were first built using the SMoG growth procedure (DeWitte & Shakhnovich, 1996) with a chosen input potential. These model protein-ligand complexes were used to construct databases from which knowledge-based protein-ligand potentials were derived. We then tested several different modifications to such potentials and evaluated their performance on their ability to reconstruct the input potential using the statistical information available from a database composed of model complexes. Our data indicate that the most significant improvement resulted from properly accounting for the following key issues when estimating the reference state: (1) the presence of significant nonenergetic effects that influence the contact frequencies and (2) the presence of correlations in contact patterns due to chemical structure. The most successful procedure was applied to derive an atom-atom potential for real protein-ligand complexes. Despite the simplicity of the model (pairwise contact potential with a single interaction distance), the derived binding free energies showed a statistically significant correlation (approximately 0.65) with experimental binding scores for a diverse set of complexes.

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Selected References

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  1. Ajay, Murcko M. A. Computational methods to predict binding free energy in ligand-receptor complexes. J Med Chem. 1995 Dec 22;38(26):4953–4967. doi: 10.1021/jm00026a001. [DOI] [PubMed] [Google Scholar]
  2. Babine Robert E., Bender Steven L. Molecular Recognition of Proteinminus signLigand Complexes: Applications to Drug Design. Chem Rev. 1997 Aug 5;97(5):1359–1472. doi: 10.1021/cr960370z. [DOI] [PubMed] [Google Scholar]
  3. Bernstein F. C., Koetzle T. F., Williams G. J., Meyer E. F., Jr, Brice M. D., Rodgers J. R., Kennard O., Shimanouchi T., Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol. 1977 May 25;112(3):535–542. doi: 10.1016/s0022-2836(77)80200-3. [DOI] [PubMed] [Google Scholar]
  4. Betancourt M. R., Thirumalai D. Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci. 1999 Feb;8(2):361–369. doi: 10.1110/ps.8.2.361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boriack-Sjodin P. A., Zeitlin S., Chen H. H., Crenshaw L., Gross S., Dantanarayana A., Delgado P., May J. A., Dean T., Christianson D. W. Structural analysis of inhibitor binding to human carbonic anhydrase II. Protein Sci. 1998 Dec;7(12):2483–2489. doi: 10.1002/pro.5560071201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Böhm H. J. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des. 1998 Jul;12(4):309–323. doi: 10.1023/a:1007999920146. [DOI] [PubMed] [Google Scholar]
  7. Böhm H. J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des. 1994 Jun;8(3):243–256. doi: 10.1007/BF00126743. [DOI] [PubMed] [Google Scholar]
  8. DeWitte R. S., Shakhnovich E. I. Pseudodihedrals: simplified protein backbone representation with knowledge-based energy. Protein Sci. 1994 Sep;3(9):1570–1581. doi: 10.1002/pro.5560030922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Eldridge M. D., Murray C. W., Auton T. R., Paolini G. V., Mee R. P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des. 1997 Sep;11(5):425–445. doi: 10.1023/a:1007996124545. [DOI] [PubMed] [Google Scholar]
  10. Finkelstein A. V., Badretdinov AYa, Gutin A. M. Why do protein architectures have Boltzmann-like statistics? Proteins. 1995 Oct;23(2):142–150. doi: 10.1002/prot.340230204. [DOI] [PubMed] [Google Scholar]
  11. Godzik A., Koliński A., Skolnick J. Are proteins ideal mixtures of amino acids? Analysis of energy parameter sets. Protein Sci. 1995 Oct;4(10):2107–2117. doi: 10.1002/pro.5560041016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Goldstein R. A., Luthey-Schulten Z. A., Wolynes P. G. Optimal protein-folding codes from spin-glass theory. Proc Natl Acad Sci U S A. 1992 Jun 1;89(11):4918–4922. doi: 10.1073/pnas.89.11.4918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Goldstein R. A., Luthey-Schulten Z. A., Wolynes P. G. Protein tertiary structure recognition using optimized Hamiltonians with local interactions. Proc Natl Acad Sci U S A. 1992 Oct 1;89(19):9029–9033. doi: 10.1073/pnas.89.19.9029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hinds D. A., Levitt M. Exploring conformational space with a simple lattice model for protein structure. J Mol Biol. 1994 Nov 4;243(4):668–682. doi: 10.1016/0022-2836(94)90040-x. [DOI] [PubMed] [Google Scholar]
  15. Jernigan R. L., Bahar I. Structure-derived potentials and protein simulations. Curr Opin Struct Biol. 1996 Apr;6(2):195–209. doi: 10.1016/s0959-440x(96)80075-3. [DOI] [PubMed] [Google Scholar]
  16. Kocher J. P., Rooman M. J., Wodak S. J. Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches. J Mol Biol. 1994 Feb 4;235(5):1598–1613. doi: 10.1006/jmbi.1994.1109. [DOI] [PubMed] [Google Scholar]
  17. Mirny L. A., Shakhnovich E. I. How to derive a protein folding potential? A new approach to an old problem. J Mol Biol. 1996 Dec 20;264(5):1164–1179. doi: 10.1006/jmbi.1996.0704. [DOI] [PubMed] [Google Scholar]
  18. Muegge I., Martin Y. C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem. 1999 Mar 11;42(5):791–804. doi: 10.1021/jm980536j. [DOI] [PubMed] [Google Scholar]
  19. 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. 1990 Jun 20;213(4):859–883. doi: 10.1016/s0022-2836(05)80269-4. [DOI] [PubMed] [Google Scholar]
  20. Sippl M. J., Ortner M., Jaritz M., Lackner P., Flöckner H. Helmholtz free energies of atom pair interactions in proteins. Fold Des. 1996;1(4):289–298. doi: 10.1016/S1359-0278(96)00042-9. [DOI] [PubMed] [Google Scholar]
  21. Thomas P. D., Dill K. A. Statistical potentials extracted from protein structures: how accurate are they? J Mol Biol. 1996 Mar 29;257(2):457–469. doi: 10.1006/jmbi.1996.0175. [DOI] [PubMed] [Google Scholar]
  22. Verkhivker G., Appelt K., Freer S. T., Villafranca J. E. Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. Protein Eng. 1995 Jul;8(7):677–691. doi: 10.1093/protein/8.7.677. [DOI] [PubMed] [Google Scholar]
  23. Wallqvist A., Jernigan R. L., Covell D. G. A preference-based free-energy parameterization of enzyme-inhibitor binding. Applications to HIV-1-protease inhibitor design. Protein Sci. 1995 Sep;4(9):1881–1903. doi: 10.1002/pro.5560040923. [DOI] [PMC free article] [PubMed] [Google Scholar]

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