title: Benchmarking pK(a) prediction.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Edward Jenner Institute for Vaccine Research, Compton, Berkshire, RG20 7NN, UK. m.davies@mail.cryst.bbk.ac.uk
authors.forename: Matthew N
authors.initials: MN
authors.lastname: Davies
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Toseland
authors.forename: David S
authors.initials: DS
authors.lastname: Moss
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D002384:Catalysis; D007700:Kinetics; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012044:Regression Analysis; D012984:Software; D012995:Solubility
chemical_list: D011506:Proteins