PKa prediction

Perrin, D. D. Dempsey, B. Serjeant, E. P.,pKa Prediction for Organic Acids and Bases, Chapman Hall, London, 1981. 

Another advantage of PB based pKa calculations is that effects of electrolytes are readily accounted for in the PB equation. The Coulombic contribution in conjunction with salt dependence to the abnormally depressed pAVs of histidine in staphylococcal nuclease has been experimentally tested [56], Recently, the methodology used in the PB calculations (Eqs. 10-11 and 10-12) has been combined with the generalized Born (GB) implicit solvent model [94] to offer pKa predictions at a reduced computational cost [52],... 

Several remedies have been suggested for improving the PB based pKa prediction methods. Most of them are based on strategies that combine conformational flexibility with the PB calculation. You and Bashford included multiple conformers by systematically scanning the side chain torsion angles [107], Alexov and Gunner used Monte-Carlo protocol to sample positions of hydroxyl and other polar protons [1], This method, referred to as the multi-conformation continuum electrostatic (MCCE), was later extended to include rotamers for residues that have strong electrostatic... 

DD Perrin, B Dempsey, EP Serjeant, pKa prediction for organic acids and bases. Chapman and Hall New York, 1981. 

Milletti, E., Storchi, L., Sforna, G. and Cruciani, G. (2007) New and original pKa prediction method using grid molecular interaction fields. Journal of Chemical Information and Modeling, 47,... 

The rather fundamental and almost unparameterized COSMO-RS approach also allows for the calculation of pKa in aqueous mixtures and other solvents. As an example we applied it to DMSO (see Fig. 10.6), achieving about the same accuracy as in water, but a slightly higher slope. It is not clear whether the difference in slope is of physical significance. Thus, COSMO-RS may be used for pKa prediction in different solvents, but unfortunately a parameterization is required for each solvent. 

Bas DC, Rogers DM, Jensen JH (2008) Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins 73 765-783. doi 10.1002/prot.22102 Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pKa values. Proteins 61 704-721. doi 10.1002/prot. 20660 Olsson MH, Sondergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3 consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7 525-537... 

The pKa prediction has not yet reached a high level of accuracy. An error of +0.5 pK units is to be expected, but there will of course be situations where errors will be higher, particularly with compounds that are dissimilar from the compounds that were studied to formulate the prediction system. Some pKa prediction packages are trainable such that experimental values for related compounds (or indeed the compounds themselves) can be stored and used to increase accuracy in subsequent predictions. Besides, the pKa itself is not a solid physical constant of a particular compound its value is dependent on many environmental conditions, such as solution media, dielectric constant, temperature, ionic strength, and even method of measurement. The average error for the literature values obtained in different laboratories for the same compound has been on the order of 0.5 pH units. 

The Merck Index 12 edition, Budavari, S., Ed., Merck Co., Inc. White House Station, NJ 1996. Perrin, D. D. Dempsey, B. Serjeant, E. P. pKa Prediction for Organic Acids and Bases Chapman and Hall New York 1981. Serjeant, E. P. Dempsey, B. Ionization Constants of Organic Acids in Aqueous Solution Pergamon New York 1979. CRC Handbook of Chemistry and Physics 76th ed. CRC Press New York 1995—1996. Lange s Handbook of Chemistry, Dean, J. A., Ed, 14th edition McGraw-Hill New York, 1992. 

A number of approaches to predict ionization based on structure have been published (for a review see Ref. [50]), and some of these are commercially available. Predictions tend to be good for structures with already known and measured functional groups. However, predictions can be poor for new innovative structures. Nevertheless, pKa predictions can still be used to drive a project in the desired direction, and rank order of the compounds is often correct. More recently, training algorithms have also become available, which use in-house data to improve the predictions. This is obviously the way forward. 

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