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Dependency of ligand free energy landscapes on charge parameters and solvent models

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Abstract

We performed replica-exchange molecular dynamics (REMD) simulations of six ligands to examine the dependency of their free energy landscapes on charge parameters and solvent models. Six different charge parameter sets for each ligand were first generated by RESP and AM1-BCC methods using three different conformations independently. RESP charges showed some conformational dependency. On the other hand, AM1-BCC charges did not show conformational dependency and well reproduced the overall trend of RESP charges. The free energy landscapes obtained from the REMD simulations of ligands in vacuum, Generalized-Born (GB), and TIP3P solutions were then analyzed. We found that even small charge differences can produce qualitatively different landscapes in vacuum condition, but the differences tend to be much smaller under GB and TIP3P conditions. The simulations in the GB model well reproduced the landscapes in the TIP3P model using only a fraction of the computational cost. The protein-bound ligand conformations were rarely the global minimum states, but similar conformations were found to exist in aqueous solution without proteins in regions close to the global minimum, local minimum or intermediate states.

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References

  1. Boström J (2001) Reproducing the conformations of protein-bound ligands: a critical evaluation of several popular conformational searching tools. J Comput Aided Mol Des 15:1137–1152

    Article  Google Scholar 

  2. Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 47:2499–2510

    Article  CAS  Google Scholar 

  3. Diller DJ, Merz KM (2002) Can we separate active from inactive conformations. J Comput Aided Mol Des 16:105–112

    Article  CAS  Google Scholar 

  4. Boström J, Greenwood JR, Gottfries J (2003) Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J Mol Graph Model 21:449–462

    Article  Google Scholar 

  5. Agrafiotis DK, Gibbs AC, Zhu F, Izrailev S, Martin E (2007) Conformational sampling of bioactive molecules: a comparative study. J Chem Inf Model 47:1067–1086

    Article  CAS  Google Scholar 

  6. Chen I, Foloppe N (2008) Conformational sampling of druglike molecules with MOE and catalyst: implications for pharmacophore modeling and virtual screening. J Chem Inf Model 48:1773–1791

    Article  CAS  Google Scholar 

  7. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  Google Scholar 

  8. Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25:247–260

    Article  Google Scholar 

  9. Bayly CI, Cieplak P, Cornell W, 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

    Article  CAS  Google Scholar 

  10. Cieplak P, Cornell WD, Bayly C, Kollman PA (1995) Application of the multimolecule and multiconformational RESP methodology to biopolymers: charge derivation for DNA, RNA and proteins. J Comput Chem 16:1357–1377

    Article  CAS  Google Scholar 

  11. Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method. J Comput Chem 21:132–146

    Article  CAS  Google Scholar 

  12. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  13. Fujitani H, Tanida Y, Matsuura A (2009) Massively parallel computation of absolute binding free energy with well-equilibrated states. Phys Rev E 79:021914

    Article  Google Scholar 

  14. Mobley DL, Graves AP, Chodera JD, McReynolds AC, Shoichet BK, Dill KA (2007) Predicting absolute ligand binding free energies to a simple model site. J Mol Biol 371:1118–1134

    Article  CAS  Google Scholar 

  15. Boyce SE, Mobley DL, Rockline GJ, Graves AP, Dill KA, Shoichet BK (2009) Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site. J Mol Biol 394:747–763

    Article  CAS  Google Scholar 

  16. Deng Y, Roux B (2008) Computation of binding free energy with molecular dynamics and grand canonical Monte Carlo simulations. J Chem Phys 128:115103

    Article  Google Scholar 

  17. Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42

    Article  CAS  Google Scholar 

  18. Kokubo H, Pettitt MB (2007) Preferential solvation in urea solutions at different concentrations: properties from simulation studies. J Phys Chem B 111:5233–5242

    Article  CAS  Google Scholar 

  19. Kokubo H, Rosgen J, Bolen D, Pettitt MB (2007) Molecular basis of the apparent near ideality of urea solutions. Biophys J 93:3392–3407

    Article  CAS  Google Scholar 

  20. 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

    Article  CAS  Google Scholar 

  21. Feig M, Onufriev A, Lee MS, Im W, Case DA, Brooks CL III (2004) Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures. J Comput Chem 25:265–284

    Article  CAS  Google Scholar 

  22. 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

    Article  CAS  Google Scholar 

  23. Chemical Computing Group Inc., Montreal, Canada (2008) MOE (Molecular Operating Environment) version 2008.10

  24. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  CAS  Google Scholar 

  25. DeLano WL (2002) The PyMOL molecular graphics system. DeLano Scientific, Palo Alto

    Google Scholar 

  26. Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  Google Scholar 

  27. Ryckaert J, 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

    Article  CAS  Google Scholar 

  28. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  CAS  Google Scholar 

  29. 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

    Article  CAS  Google Scholar 

  30. Hukushima K, Nemoto K (1996) Exchange Monte Carlo method and application to spin glass simulations. J Phys Soc Jpn 65:1604–1608

    Article  CAS  Google Scholar 

  31. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151

    Article  CAS  Google Scholar 

  32. Mitsutake A, Sugita Y, Okamoto Y (2001) Generalized-ensemble algorithms for molecular simulations of biopolymers. Peptide Sci 60:96–123

    CAS  Google Scholar 

  33. Teeter MM, Case DA (1990) Harmonic and quasiharmonic descriptions of crambin. J Phys Chem 94:8091–8097

    Article  CAS  Google Scholar 

  34. Kitao A, Hirata F, Go N (1991) The effects of solvent on the conformation and the collective motions of protein: normal mode analysis and molecular dynamics simulations of melittin in water and in vacuum. Chem Phys 158:447–472

    Article  CAS  Google Scholar 

  35. Garcia AE (1992) Large-amplitude nonlinear motions in proteins. Phys Rev Lett 68:2696–2699

    Article  CAS  Google Scholar 

  36. Abagyan R, Argos P (1992) Optimal protocol and trajectory visualization for conformational searches of peptides and proteins. J Mol Biol 225:519–532

    Article  CAS  Google Scholar 

  37. Amadei A, Linssen ABM, Berendsen HJC (1993) Essential dynamics of proteins. Proteins 17:412–425

    Article  CAS  Google Scholar 

  38. Kitao A, Go N (1999) Investigating protein dynamics in collective coordinate space. Curr Opin Struct Biol 9:164–169

    Article  CAS  Google Scholar 

  39. Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20:217–230

    Article  CAS  Google Scholar 

  40. Klimovich PV, Mobley DL (2010) Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations. J Comput Aided Mol Des 24 (in press)

  41. Mobley DL, Dumont E, Chodera JD, Dill KA (2007) Comparison of charge models for fixed-charge force fields:  small-molecule hydration free energies in explicit solvent. J Phys Chem B 111:2242–2254

    Article  CAS  Google Scholar 

  42. Mobley DL, Bayly CI, Cooper MD, Shirts MR, Dill KA (2009) Small molecule hydration free energies in explicit solvent: an extensive test of fixed-charge atomistic simulations. J Chem Theory Comput 5:350–358

    Article  CAS  Google Scholar 

  43. Mobley DL, Dill KA, Chodera JD (2008) Treating entropy and conformational changes in implicit solvent simulations of small molecules. J Phys Chem B 112:938–946

    Article  CAS  Google Scholar 

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Acknowledgments

We are grateful for the insight, discussions and collegiality provided by Dr. Masaki Tomimoto. The authors also thank Dr. Douglas Cary for reading the manuscript. The simulations and computations were performed on the TSUBAME Grid Cluster at Global Scientific Information and Computing Center of Tokyo Institute of Technology supported by the MEXT Open Advanced Research Facilities Initiative. This work was supported, in part, by Grants-in-Aid for Scientific Research on Innovative Areas (“Fluctuations and Biological Functions”) and for the Next-Generation Super Computing Project, Nanoscience Program from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.

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Correspondence to Hironori Kokubo.

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Yuko Okamoto and Hironori Kokubo contributed equally to this work.

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Okamoto, Y., Tanaka, T. & Kokubo, H. Dependency of ligand free energy landscapes on charge parameters and solvent models. J Comput Aided Mol Des 24, 699–712 (2010). https://doi.org/10.1007/s10822-010-9367-z

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  • DOI: https://doi.org/10.1007/s10822-010-9367-z

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