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An empirical model for electrostatic interactions in proteins incorporating multiple geometry‐dependent dielectric constants
Author(s) -
Wisz Michael S.,
Hellinga Homme W.
Publication year - 2003
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10332
Subject(s) - electrostatics , dielectric , computation , static electricity , pairwise comparison , polar , hydrogen bond , relaxation (psychology) , charge (physics) , chemistry , statistical physics , work (physics) , chemical physics , geometry , molecular physics , physics , mathematics , thermodynamics , molecule , algorithm , quantum mechanics , psychology , social psychology , statistics
Here we introduce an electrostatic model that treats the complexity of electrostatic interactions in a heterogeneous protein environment by using multiple parameters that take into account variations in protein geometry, local structure, and the type of interacting residues. The optimal values for these parameters were obtained by fitting the model to a large dataset of 260 experimentally determined p K a values distributed over 41 proteins. We obtain fits between the calculated and observed values that are significantly better than the null model. The model performs well on the groups that exhibit large p K a shifts from solution values in response to the protein environment and compares favorably with other, successful continuum models. The empirically determined values of the parameters correlate well with experimentally observed contributions of hydrogen bonds and ion pairs as well as theoretically predicted magnitudes of charge‐charge and charge‐polar interactions. The magnitudes of the dielectric constants assigned to different regions of the protein rank according to the strength of the relaxation effects expected for the core, boundary, and surface. The electrostatic interactions in this model are pairwise decomposable and can be calculated rapidly. This model is therefore well suited for the large computations required for simulating protein properties and especially for prediction of mutations for protein design. Proteins 2003;51:360–377. © 2003 Wiley‐Liss, Inc.