Benchmarking pK(a) prediction

BMC Biochem. 2006 Jun 2;7:18. doi: 10.1186/1471-2091-7-18.

Abstract

Background: pKa values are a measure of the protonation of ionizable groups in proteins. Ionizable groups are involved in intra-protein, protein-solvent and protein-ligand interactions as well as solubility, protein folding and catalytic activity. The pKa shift of a group from its intrinsic value is determined by the perturbation of the residue by the environment and can be calculated from three-dimensional structural data.

Results: Here we use a large dataset of experimentally-determined pKas to analyse the performance of different prediction techniques. Our work provides a benchmark of available software implementations: MCCE, MEAD, PROPKA and UHBD. Combinatorial and regression analysis is also used in an attempt to find a consensus approach towards pKa prediction. The tendency of individual programs to over- or underpredict the pKa value is related to the underlying methodology of the individual programs.

Conclusion: Overall, PROPKA is more accurate than the other three programs. Key to developing accurate predictive software will be a complete sampling of conformations accessible to protein structures.

MeSH terms

  • Catalysis
  • Kinetics
  • Protein Conformation
  • Protein Folding
  • Proteins / chemistry*
  • Proteins / metabolism*
  • Regression Analysis
  • Software
  • Solubility

Substances

  • Proteins