Conflict of interest: The authors state that JMW and AN are employees of OpenEye Scientific Software, Inc. which sells products based on the technology used in this work. Results of this paper were from software made and sold by this company.
Application of the Gaussian dielectric boundary in Zap to the prediction of protein pKa values†
Article first published online: 9 JUN 2011
Copyright © 2011 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Special Issue: Protein Electrostatics Calculations: Critical Assessment of Progress and Problems
Volume 79, Issue 12, pages 3400–3409, December 2011
How to Cite
Word, J. M. and Nicholls, A. (2011), Application of the Gaussian dielectric boundary in Zap to the prediction of protein pKa values. Proteins, 79: 3400–3409. doi: 10.1002/prot.23079
- Issue published online: 10 NOV 2011
- Article first published online: 9 JUN 2011
- Accepted manuscript online: 10 MAY 2011 09:58AM EST
- Manuscript Accepted: 4 MAY 2011
- Manuscript Revised: 23 MAR 2011
- Manuscript Received: 10 JAN 2011
- estimating pKa values;
- smooth dielectric boundary;
- staphylococcal nuclease
The results of two rounds of blind pKa predictions for ionizable residues in staphylococcal nuclease using OpenEye's legacy protein pKa prediction program based on the Zap Poisson–Boltzmann solver were submitted to the 2009 prediction challenge organized by the Protein pKa Cooperative and first round predictions were discussed at the corresponding June 2009 Telluride conference. To better understand these results, 21 additional sets of predictions were performed with the same program, varying the internal dielectric, reference pKa, partial charge set, and dielectric boundary. The internal dielectric (ϵp) and dielectric boundary were the two most important factors contributing to the quality of the predictions. Although the lowest overall errors were observed with a molecular dielectric boundary at ϵp = 8, predictions using a smooth Gaussian dielectric boundary performed almost as well at lower ϵp values because the Gaussian boundary implicitly accounts for a significant level of solvent penetration. Improved pKa predictions with the Gaussian boundary methodology will require better prediction and modeling of structural changes due to changes in ionization state, perhaps without resorting to the more exhaustive sampling of conformational states used by other recent continuum methods. Proteins 2011; © 2011 Wiley-Liss, Inc.