Skip to main content

Advertisement

Log in

An improved method to predict the entropy term with the MM/PBSA approach

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

Abstract

A method is suggested to calculate improved entropies within the MM/PBSA approach (molecular mechanics combined with Poisson–Boltzmann and surface area calculations) to estimate protein–ligand binding affinities. In the conventional approach, the protein is truncated outside ~8 Å from the ligand. This system is freely minimised using a distance-dependent dielectric constant (to simulate the removed protein and solvent). However, this can lead to extensive changes in the molecular geometry, giving rise to a large standard deviation in this term. In our new approach, we introduce a buffer region ~4 Å outside the truncated protein (including solvent molecules) and keep it fixed during the minimisation. Thereby, we reduce the standard deviation by a factor of 2–4, ensuring that the entropy term no longer limits the precision of the MM/PBSA predictions. The new method is tested for the binding of seven biotin analogues to avidin, eight amidinobenzyl-indole-carboxamide inhibitors to factor Xa, and two substrates to cytochrome P450 3A4 and 2C9. It is shown that it gives more stable results and often improved predictions of the relative binding affinities.

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

Similar content being viewed by others

References

  1. Gohlke H, Klebe G (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 41:2644–2676. doi:10.1002/1521-3773(20020802)41:15<2644::AID-ANIE2644>3.0.CO;2-O

    Article  CAS  Google Scholar 

  2. Gilson MK, Given JA, Bush BL, McCammon JA (1997) The statistical thermodynamic basis for computation of binding affinities: a critical review. Biophys J 72:1047–1069

    CAS  Google Scholar 

  3. Beveridge DL, Dicapua FM (1989) Free-energy via molecular simulation—applications to chemical and biomolecular systems. Annu Rev Biophys Biophys Chem 18:431–492. doi:10.1146/annurev.bb.18.060189.002243

    Article  CAS  Google Scholar 

  4. Miyamoto S, Kollman PA (1993) Absolute and relative binding free energy calculations of the interaction of biotin and its analogs with streptavidin using molecular dynamics/free energy perturbation approaches. Proteins 16:226–245. doi:10.1002/prot.340160303

    Article  CAS  Google Scholar 

  5. Hansson T, Marelius J, Åqvist J (1998) Ligand binding affinity prediction by linear interaction energy methods. J Comput Aided Mol Des 12:27–35. doi:10.1023/A:1007930623000

    Article  CAS  Google Scholar 

  6. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L et al (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897. doi:10.1021/ar000033j

    Article  CAS  Google Scholar 

  7. Gilson MK, Honig B (1998) Calculations of the total electrostatic energy of a macromolecular system: solvation energies, binding energies, and conformational analysis. Proteins, Struct Funct Gen 4:7–18

    Article  Google Scholar 

  8. Hermann RB (1972) Theory of hydrophobic bonding. II. Correlation of hydrocarbon solubility in water with solvent cavity surface area. J Phys Chem 76:2754–2759. doi:10.1021/j100663a023

    Article  CAS  Google Scholar 

  9. Swanson JMJ, Henchman RH, McCammon JA (2004) Revisiting free energy calculations: a theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophys J 86:67–74

    Article  CAS  Google Scholar 

  10. Kuhn B, Kollman PA (2000) Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models. J Med Chem 43:3786–3791. doi:10.1021/jm000241h

    Article  CAS  Google Scholar 

  11. Huo S, Wang J, Cieplak P, Kollman PA, Kuntz ID (2002) Molecular dynamics and free energy analyses of cathepsin D-inhibitor interactions: insight into structure-based ligand design. J Med Chem 45:1412–1419. doi:10.1021/jm010338j

    Article  CAS  Google Scholar 

  12. Brown SP, Muchmore SW (2006) High-throughput calculation of protein-ligand binding affinities: modification and adaptation of the MM-PBSA protocol to enterprise grid computing. J Chem Inf Model 46:999–1005. doi:10.1021/ci050488t

    Article  CAS  Google Scholar 

  13. Weis A, Katebzadeh K, Söderhjelm P, Nilsson I, Ryde U (2006) Ligand affinities predicted with the MM/PBSA method: dependence on the simulation method and the force field. J Med Chem 49:6596–6606. doi:10.1021/jm0608210

    Article  CAS  Google Scholar 

  14. Pearlman DA (2005) Evaluating the molecular mechanics Poisson–Boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase. J Med Chem 48:7796–7807. doi:10.1021/jm050306m

    Article  CAS  Google Scholar 

  15. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation and use of the MM-PBSA approach for drug discovery. J Med Chem 48:4040–4048. doi:10.1021/jm049081q

    Article  CAS  Google Scholar 

  16. Matter H, Defossa E, Heinelt U, Blohm P-M, Schneider D, Müller A, Herok S, Schreuder H, Liesum A, Brachvogel V, Lönze P, Walser A, Al-Obeidi F, Wildgoose P (2002) J Med Chem 45:2749–2769. doi:10.1021/jm0111346

    Article  CAS  Google Scholar 

  17. Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174. doi:10.1002/jcc.20035

    Article  CAS  Google Scholar 

  18. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr, Ferguson DM et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197. doi:10.1021/ja00124a002

    Article  CAS  Google Scholar 

  19. Wang J, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21:1049–1074. doi:10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F

    Article  CAS  Google Scholar 

  20. Bayly CI, Cieplak P, Cornell WD, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges—the RESP model. J Phys Chem 97:10269–10280. doi:10.1021/j100142a004

    Article  CAS  Google Scholar 

  21. Besler BH, Merz KM, Kollman PA (1990) Atomic charges derived from semiempirical methods. J Comput Chem 11:431–439. doi:10.1002/jcc.540110404

    Article  CAS  Google Scholar 

  22. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and interpretation of protein pKa values. Proteins 61:704–721. doi:10.1002/prot.20660

    Article  CAS  Google Scholar 

  23. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. doi:10.1063/1.445869

    Article  CAS  Google Scholar 

  24. Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341. doi:10.1016/0021-9991(77)90098-5

    Article  CAS  Google Scholar 

  25. Case DA, Darden TA, Cheatham TEIII, Simmerling CL, Wang J, Duke RE et al (2006) AMBER 9. University of California, San Francisco

    Google Scholar 

  26. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. doi:10.1063/1.448118

    Article  CAS  Google Scholar 

  27. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092. doi:10.1063/1.464397

    Article  CAS  Google Scholar 

  28. Söderhjelm P, Ryde U (2008) Conformational dependence of charges in protein simulations. J Comput Chem. doi:10.1002/jcc.21097

    Google Scholar 

  29. Rydberg P, Hansen SM, Kongsted J, Norrby P-O, Olsen L, Ryde U (2008) J Chem Theory Comput 4:673–681. doi:10.1021/ct700313j

    Article  CAS  Google Scholar 

  30. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W et al (2003) A point-charge force field for molecular mechanics simulations of proteins. J Comput Chem 24:1999–2012. doi:10.1002/jcc.10349

    Article  CAS  Google Scholar 

  31. Rocchia W, Alexov E, Honig B (2001) Extending the applicability of the nonlinear Poisson–Boltzmann equation: multiple dielectric constants and multivalent ions. J Phys Chem B 105:6507–6514. doi:10.1021/jp010454y

    Article  CAS  Google Scholar 

  32. Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Phys Chem 98:1978–1988. doi:10.1021/j100058a043

    Article  CAS  Google Scholar 

  33. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized Born model. Proteins 55:383–394. doi:10.1002/prot.20033

    Article  CAS  Google Scholar 

  34. Jensen F (1999) Introduction to computational chemistry. Wiley, Chichester

    Google Scholar 

  35. Siegbahn PEM, Blomberg MRA (2000) Transition-metal systems in biochemistry studied by high-accuracy quantum chemical methods. Chem Rev 100:421–437. doi:10.1021/cr980390w

    Article  CAS  Google Scholar 

  36. Wang J, Morin P, Wang W, Kollman PA (2001) Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of Efavirenz by docking and MM-PBSA. J Am Chem Soc 123:5221–5230. doi:10.1021/ja003834q

    Article  CAS  Google Scholar 

  37. Amzel LM (1997) Loss of translational entropy in binding, folding, and catalysis. Proteins Struct Funct Gen 28:144–149. doi:10.1002/(SICI)1097-0134(199706)28:2<144::AID-PROT2>3.0.CO;2-F

    Article  CAS  Google Scholar 

  38. Gohlke R, Case DA (2004) Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf. J Comput Chem 25:238–250. doi:10.1002/jcc.10379

    Article  CAS  Google Scholar 

  39. Suenaga A, Takada N, Hatakeyama M, Ichikawa M, Yu X, Tomii K et al (2005) Novel mechanism of interaction of p85 subunit of phosphatidylinositol 3-Kinase and ErbB3 receptor-derived phosphotyrosyl peptides. J Biol Chem 280:1321–1326. doi:10.1074/jbc.M410436200

    Article  CAS  Google Scholar 

  40. Searle MS, Williams DH (1992) The cost of conformational order: entropy changes in molecular associations. J Am Chem Soc 114:10690. doi:10.1021/ja00053a002

    Article  CAS  Google Scholar 

  41. Böhm H-J (1994) 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 8:243–256. doi:10.1007/BF00126743

    Article  Google Scholar 

  42. Lazardis T, Masunov A, Gandolfo F (2002) Contributions to the binding free energy of ligands to avidin and streptavidin. Proteins Struct Funct Gen 47:194–208. doi:10.1002/prot.10086

    Article  CAS  Google Scholar 

  43. Giordanetto F, Cotesta S, Catana C, Trosset J-Y, Vulpetti A, Stouten PFW et al (2004) Novel scoring functions comprising QXP, SASA, and protein side-chain entropy terms. Chem Inf Comput Sci 44:882–893. doi:10.1021/ci0499626

    CAS  Google Scholar 

  44. Raha K, Merz KM (2004) A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding. J Am Chem Soc 126:1020–1021. doi:10.1021/ja038496i

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This investigation has been supported by grants from the Swedish research council and the Villum Kann Rasmussen foundation (Denmark), and by computer resources of Lunarc at Lund University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ulf Ryde.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kongsted, J., Ryde, U. An improved method to predict the entropy term with the MM/PBSA approach. J Comput Aided Mol Des 23, 63–71 (2009). https://doi.org/10.1007/s10822-008-9238-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-008-9238-z

Keywords

Navigation