Skip to main content
Log in

Benchmarking pKa prediction methods for Lys115 in acetoacetate decarboxylase

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

Abstract

Three different pKa prediction methods were used to calculate the pKa of Lys115 in acetoacetate decarboxylase (AADase): the empirical method PROPKA, the multiconformation continuum electrostatics (MCCE) method, and the molecular dynamics/thermodynamic integration (MD/TI) method with implicit solvent. As expected, accurate pKa prediction of Lys115 depends on the protonation patterns of other ionizable groups, especially the nearby Glu76. However, since the prediction methods do not explicitly sample the protonation patterns of nearby residues, this must be done manually. When Glu76 is deprotonated, all three methods give an incorrect pKa value for Lys115. If protonated, Glu76 is used in an MD/TI calculation, the pKa of Lys115 is predicted to be 5.3, which agrees well with the experimental value of 5.9. This result agrees with previous site-directed mutagenesis studies, where the mutation of Glu76 (negative charge when deprotonated) to Gln (neutral) causes no change in Km, suggesting that Glu76 has no effect on the pKa shift of Lys115. Thus, we postulate that the pKa of Glu76 is also shifted so that Glu76 is protonated (neutral) in AADase.

Simulated abundances of protonated species as pH is varied

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pKa values. Proteins Struct Funct Bioinforma 61(4):704–721

    Article  CAS  Google Scholar 

  2. Bas DC, Rogers DM, Jensen JH (2008) Very fast prediction and rationalization of pKa values for protein–ligand complexes. Proteins Struct Funct Bioinforma 73(3):765–783

    Article  CAS  Google Scholar 

  3. He Y, Xu J, Pan X-M (2007) A statistical approach to the prediction of pKa values in proteins. Proteins Struct Funct Bioinforma 69(1):75–82

    Article  CAS  Google Scholar 

  4. Bashford D, Karplus M (1990) pKa’s of ionizable groups in proteins: atomic detail from a continuum electrostatic model. Biochemistry 29(44):10219–10225

    Article  CAS  Google Scholar 

  5. Yang A-S, Honig B (1993) On the pH dependence of protein stability. J Mol Biol 231(2):459–474

    Article  CAS  Google Scholar 

  6. Yang A-S, Gunner MR, Sampogna R, Sharp K, Honig B (1993) On the calculation of pKas in proteins. Proteins Struct Funct Genet 15(3):252–265

    Article  CAS  Google Scholar 

  7. Jensen JH, Li H, Robertson AD, Molina PA (2005) Prediction and rationalization of protein pK a values using QM and QM/MM methods. J Phys Chem A 109(30):6634–6643

    Article  CAS  Google Scholar 

  8. Alexov EG, Gunner MR (1997) Incorporating protein conformational flexibility into the calculation of pH-dependent protein properties. Biophys J 72(5):2075–2093

    Article  CAS  Google Scholar 

  9. Georgescu RE, Alexov EG, Gunner MR (2002) Combining conformational flexibility and continuum electrostatics for calculating pKas in proteins. Biophys J 83(4):1731–1748

    Article  CAS  Google Scholar 

  10. Song Y, Mao J, Gunner MR (2009) MCCE2: improving protein pK a calculations with extensive side chain rotamer sampling. J Comput Chem 30(14):2231–2247

    CAS  Google Scholar 

  11. Bashford D, Gerwert K (1992) Electrostatic calculations of the pKa values of ionizable groups in bacteriorhodopsin. J Mol Biol 224(2):473–486

    Article  CAS  Google Scholar 

  12. Bashford D (2004) Macroscopic electrostatic models for protonation states in proteins. Front Biosci 9:1082–1099

    Article  CAS  Google Scholar 

  13. Simonson T, Carlsson J, Case DA (2004) Proton binding to proteins: pK a calculations with explicit and implicit solvent models. J Am Chem Soc 126(13):4167–4180

    Article  CAS  Google Scholar 

  14. Jones DT, Woods DR (1986) Acetone-butanol fermentation revisited. Microbiol Rev 50(4):484–524

    CAS  Google Scholar 

  15. Hamilton GA, Westheimer FH (1959) On the mechanism of the enzymatic decarboxylation of acetoacetate I. J Am Chem Soc 81(23):6332–6333

    Article  CAS  Google Scholar 

  16. Fridovich I, Westheimer FH (1962) On the mechanism of the enzymatic decarboxylation of acetoacetate. II J Am Chem Soc 84(16):3208–3209

    Article  CAS  Google Scholar 

  17. Laursen RA, Westheimer FH (1966) The active site of acetoacetate decarboxylase I. J Am Chem Soc 88(14):3426–3430

    Article  CAS  Google Scholar 

  18. Westheimer FH (1995) Coincidences, decarboxylation, and electrostatic effects. Tetrahedron 51(1):3–20

    Article  CAS  Google Scholar 

  19. Frey PA, Kokesh FC, Westheimer FH (1971) Reporter group at the active site of acetoacetate decarboxylase. I. Ionization constant of the nitrophenol. J Am Chem Soc 93(26):7266–7269

    Article  CAS  Google Scholar 

  20. Kokesh FC, Westheimer FH (1971) Reporter group at the active site of acetoacetate decarboxylase. II. Ionization constant of the amino group. J Am Chem Soc 93(26):7270–7274

    Article  CAS  Google Scholar 

  21. Westheimer FH, Schmidt DE (1971) pK of the lysine amino group at the active site of acetoacetate decarboxylase. Biochemistry 10(7):1249–1253

    Article  CAS  Google Scholar 

  22. Ho M-C, Ménétret J-F, Tsuruta H, Allen KN (2009) The origin of the electrostatic perturbation in acetoacetate decarboxylase. Nature 459(7245):393–397

    Article  CAS  Google Scholar 

  23. Schrodinger LLC (2015) The PyMOL Molecular Graphics System, version 1.3 r1. Schrödinger, LLC, New York

  24. Nicholls A, Honig B (1991) A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J Comput Chem 12(4):435–445

    Article  CAS  Google Scholar 

  25. 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(28):6507–6514

    Article  CAS  Google Scholar 

  26. Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Crowley M, Walker RC, Zhang W et al. (2008) Amber 10. University of California, San Fransisco

  27. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized Born model. Proteins Struct Funct Bioinforma 55(2):383–394

    Article  CAS  Google Scholar 

  28. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T et al (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24(16):1999–2012

    Article  CAS  Google Scholar 

  29. Snyder MA, Chatterjee A, Vlachos DG (2005) Net-event kinetic Monte Carlo for overcoming stiffness in spatially homogeneous and distributed systems. Comput Chem Eng 29(4):701–712

    Article  CAS  Google Scholar 

  30. Tighezza A, Aldhayan D, Almthar A (2011) Implementation of net-event Monte Carlo algorithm in chemical kinetics simulation software of complex isothermal reacting systems. J Saudi Chem Soc 15(4):351–355

    Article  CAS  Google Scholar 

  31. Ishikita H (2010) Origin of the pK a shift of the catalytic lysine in acetoacetate decarboxylase. FEBS Lett 584(15):3464–3468

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank NSERC for funding and Sharcnet and Compute Canada for computational resources.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul W. Ayers.

Additional information

This paper belongs to Topical Collection Festschrift in Honor of Henry Chermette

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 247 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Patel, A.H.G., Burger, S.K. et al. Benchmarking pKa prediction methods for Lys115 in acetoacetate decarboxylase. J Mol Model 23, 155 (2017). https://doi.org/10.1007/s00894-017-3324-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-017-3324-x

Keywords

Navigation