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Benchmarking pK(a) prediction.

Davies, Matthew N, Toseland, Christopher P, Moss, David S, Flower, Darren R (2006) Benchmarking pK(a) prediction. BMC biochemistry, 7 . p. 18. ISSN 1471-2091. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

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.

Item Type: Article
Subjects: Q Science
Divisions: Faculties > Sciences > School of Biosciences
Depositing User: Chris Toseland
Date Deposited: 07 Apr 2015 10:52 UTC
Last Modified: 29 May 2019 14:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/47864 (The current URI for this page, for reference purposes)
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