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Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation

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Abstract

The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4–7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.

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Notes

  1. uDock and auxiliary programs are available free of charge from Dr. Hooman Shadnia’s website at www.shadnia.com (2013).

References

  1. Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  Google Scholar 

  2. Hecht D, Fogel GB (2009) Computational intelligence methods for docking scores. Curr Comp-Aided Drug Des 5:56–68

    Article  CAS  Google Scholar 

  3. Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? J Chem Inf Model 49:1568–1580

    Article  CAS  Google Scholar 

  4. Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 5. Force-field-based prediction of binding affinities of ligands to proteins. J Chem Inf Model 49:2564–2571

    Article  CAS  Google Scholar 

  5. Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 153:S7–S26

    Article  CAS  Google Scholar 

  6. Corbeil CR, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 49:997–1009

    Article  CAS  Google Scholar 

  7. Yoon S, Welsh WJ (2004) Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring. J Chem Inf Comput Sci 44:88–96

    Article  CAS  Google Scholar 

  8. Stjernschantz E, Oostenbrink C (2010) Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 98:2682–2691

    Article  CAS  Google Scholar 

  9. Labute P, Williams C (2001) Flexible alignment of small molecules. J Med Chem 44:1483–1490

    Article  CAS  Google Scholar 

  10. Chan SL, Labute P (2010) Training a scoring function for the alignment of small molecules. J Chem Inf Model 50:1724–1735

    Article  CAS  Google Scholar 

  11. Schwarzl SM, Tschopp TB, Smith JC, Fischer S (2002) Can the calculation of ligand binding free energies be improved with continuum solvent electrostatics and an ideal gas entropy correction? J Comput Chem 23:1143–1149

    Article  CAS  Google Scholar 

  12. Barone V, Cossi M, Tomasi J (1997) A new definition of cavities for the computation of solvation free energies by the polarizable continuum model. J Chem Phys 107:3210–3221

    Article  CAS  Google Scholar 

  13. Fornabaio M, Cozzini P, Mozzarelli A, Abraham DJ, Kellogg GE (2003) Simple, intuitive calculations of free energy of binding for protein–ligand complexes. 2. Computational titration and pH effects in molecular models of neuraminidase—inhibitor complexes. J Med Chem 46:4487–4500

    Article  CAS  Google Scholar 

  14. Labute P (2009) Protonate3D: assignment of ionization states and hydrogen coordinates to macromolecular structures. Proteins: Struct Funct Bioinf 75:187–205

    Article  CAS  Google Scholar 

  15. Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42:5100–5109

    Article  CAS  Google Scholar 

  16. Yang JM, Chen YF, Shen TW, Kristal BS, Hsu DF (2005) Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model 45:1134–1146

    Article  CAS  Google Scholar 

  17. Lee FS, Chu Z-T, Bolger MB, Warshel A (1992) Calculations of antibody-antigen interactions: microscopic and semi-microscopic evaluation of the free energies of binding of phosphorylcholine analogs to McPC603. Protein Eng 5:215–228

    Article  CAS  Google Scholar 

  18. Aqvist J, Medina C, Samuelsson J-E (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7:385–391

    Article  CAS  Google Scholar 

  19. Hansson T, Aqvist J (1995) Estimation of binding free energies for HIV proteinase inhibitors by molecular dynamics simulations. Protein Eng 8:1137–1144

    Article  CAS  Google Scholar 

  20. Aqvist J, Hansson T (1996) On the validity of electrostatic linear response in polar solvents. J Phys Chem 100:9512–9521

    Article  Google Scholar 

  21. Huang S-Y, Zou X (2006) An iterative knowledge-based scoring function to predict protein- ligand interactions: I. Derivation of the interaction potentials. J Comp Chem 27:1866–1874

    Article  CAS  Google Scholar 

  22. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420

    Article  CAS  Google Scholar 

  23. Jain AN (1996) Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J Comput-Aided Mol Design 10:427–440

    Article  CAS  Google Scholar 

  24. Jain AN, Harris NL (1995) Park JY quantitative binding site model generation: compass applied to multiple chemotypes targeting the 5-HT1A receptor. J Med Chem 38:1295–1308

    Article  CAS  Google Scholar 

  25. Durrant JD, McCammon JA (2010) NNscore: a neural-network-based scoring function for the characterization of protein-ligand complexes. J Chem Inf Model 50:1865–1871

    Article  CAS  Google Scholar 

  26. Durrant JD, McCammon JA (2011) NNScore 2.0: a neural network receptor-ligand scoring function. J Chem Inf Model 51:2897–2903

    Article  CAS  Google Scholar 

  27. Deng W, Breneman C, Embrechts MJ (2004) Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods. J Chem Inf Comput Sci 44:699–703

    Article  CAS  Google Scholar 

  28. Ballester PJ, Mitchell JB (2010) A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169–1175

    Article  CAS  Google Scholar 

  29. Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE (2011) A machine learning based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model 51:408–419

    Article  CAS  Google Scholar 

  30. Martin E, Sullivan DC (2008) AutoShim: empirically corrected scoring functions for quantitative docking with a crystal structure and IC50 training data. J Chem Inf Model 48:861–872

    Article  CAS  Google Scholar 

  31. Martin E, Sullivan DC (2008) Surrogate autoshim: predocking into a universal ensemble kinase receptor for three dimensional activity prediction, very quickly, without a crystal structure. J Chem Inf Model 48:873–881

    Article  CAS  Google Scholar 

  32. Molecular Operating Environment (MOE) (2012) 10; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2012

  33. Asim M, El-Safiti M, Qian Y, Choueiri C, Salari S, Cheng J, Shadnia H, Bal M, Pratt MAC, Carlson KE, Katzenellenbogen JA, Wright JS, Durst T (2009) Deconstructing estradiol: removal of B-ring generates compounds which are potent and subtype-selective estrogen receptor agonists. Bioorg Med Chem Lett 19:1250–1253; [Erratum to document cited in (2009) Bioorg Med Chem Lett 19: 2605]

    Google Scholar 

  34. Wright JS, Shadnia H, Anderson JM, Durst T, Asim M, El-Safiti M, Choueiri C, Pratt MAC, Ruddy SC, Lau R, Carlson KE, Katzenellenbogen JA, O’Brien PJ, Wan L (2011) A-CD Estrogens: i. Substituent effects, hormone potency, and receptor subtype selectivity in a new family of flexible estrogenic compounds. J Med Chem 54:433–448

    Article  CAS  Google Scholar 

  35. Merz KM (2010) Limits of free energy computation for protein-ligand combinations. J Chem Theory Comput 6:1769–1776

    Article  CAS  Google Scholar 

  36. Gaussian 03, revision C.02; Gaussian, Inc.: Wallingford, CT, 2003

  37. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization and performance of MMFF94. J Comput Chem 17:490–517

    Article  CAS  Google Scholar 

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Acknowledgments

This work was funded by the Canadian Breast Cancer Foundation (Ontario Region) grants to J. S. Wright and T. Durst. We are grateful to the CBCF for support for this work.

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Authors and Affiliations

  1. Department of Chemistry, Carleton University, 1125 Colonel By Dr., Ottawa, K1S 5B6, Canada

    James S. Wright & James M. Anderson

  2. Chemical Computing Group, 1010 Sherbrooke St. W., Montreal, QC, H3A 2R7, Canada

    Hooman Shadnia

  3. Department of Chemistry, University of Ottawa, D’Iorio Hall, 10 Marie Curie St., Ottawa, K1N 6N5, Canada

    Tony Durst

  4. Department of Chemistry, University of Illinois, Urbana, IL, 61801, USA

    John A. Katzenellenbogen

Authors
  1. James S. Wright
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  2. James M. Anderson
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  3. Hooman Shadnia
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  4. Tony Durst
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  5. John A. Katzenellenbogen
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Correspondence to James S. Wright.

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Wright, J.S., Anderson, J.M., Shadnia, H. et al. Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation. J Comput Aided Mol Des 27, 707–721 (2013). https://doi.org/10.1007/s10822-013-9670-6

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