Literature DB >> 12696048

An empirical model for electrostatic interactions in proteins incorporating multiple geometry-dependent dielectric constants.

Michael S Wisz1, Homme W Hellinga.   

Abstract

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 pK(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 pK(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. Copyright 2003 Wiley-Liss, Inc.

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Year:  2003        PMID: 12696048     DOI: 10.1002/prot.10332

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  23 in total

1.  Influence of the solvent structure on the electrostatic interactions in proteins.

Authors:  Alexander Rubinstein; Simon Sherman
Journal:  Biophys J       Date:  2004-09       Impact factor: 4.033

2.  Energy functions for protein design I: efficient and accurate continuum electrostatics and solvation.

Authors:  Navin Pokala; Tracy M Handel
Journal:  Protein Sci       Date:  2004-03-09       Impact factor: 6.725

Review 3.  Progress in the prediction of pKa values in proteins.

Authors:  Emil Alexov; Ernest L Mehler; Nathan Baker; António M Baptista; Yong Huang; Francesca Milletti; Jens Erik Nielsen; Damien Farrell; Tommy Carstensen; Mats H M Olsson; Jana K Shen; Jim Warwicker; Sarah Williams; J Michael Word
Journal:  Proteins       Date:  2011-10-15

4.  One- and two-body decomposable Poisson-Boltzmann methods for protein design calculations.

Authors:  Shannon A Marshall; Christina L Vizcarra; Stephen L Mayo
Journal:  Protein Sci       Date:  2005-03-31       Impact factor: 6.725

5.  Simple electrostatic model improves designed protein sequences.

Authors:  Eric S Zollars; Shannon A Marshall; Stephen L Mayo
Journal:  Protein Sci       Date:  2006-07-05       Impact factor: 6.725

6.  Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design.

Authors:  Louis A Clark; P Ann Boriack-Sjodin; John Eldredge; Christopher Fitch; Bethany Friedman; Karl J M Hanf; Matthew Jarpe; Stefano F Liparoto; You Li; Alexey Lugovskoy; Stephan Miller; Mia Rushe; Woody Sherman; Kenneth Simon; Herman Van Vlijmen
Journal:  Protein Sci       Date:  2006-04-05       Impact factor: 6.725

7.  A summary of the measured pK values of the ionizable groups in folded proteins.

Authors:  Gerald R Grimsley; J Martin Scholtz; C Nick Pace
Journal:  Protein Sci       Date:  2009-01       Impact factor: 6.725

8.  The pH-dependent conformational states of kyotorphin: a constant-pH molecular dynamics study.

Authors:  Miguel Machuqueiro; António M Baptista
Journal:  Biophys J       Date:  2006-12-15       Impact factor: 4.033

9.  Computing protein stabilities from their chain lengths.

Authors:  Kingshuk Ghosh; Ken A Dill
Journal:  Proc Natl Acad Sci U S A       Date:  2009-06-17       Impact factor: 11.205

Review 10.  Computer-aided design of functional protein interactions.

Authors:  Daniel J Mandell; Tanja Kortemme
Journal:  Nat Chem Biol       Date:  2009-11       Impact factor: 15.040

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