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An improved pairwise decomposable finite‐difference Poisson–Boltzmann method for computational protein design
Author(s) -
Vizcarra Christina L.,
Zhang Naigong,
Marshall Shan A.,
Wingreen Ned S.,
Zeng Chen,
Mayo Stephen L.
Publication year - 2008
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20878
Subject(s) - pairwise comparison , boltzmann constant , poisson distribution , computational chemistry , dielectric , computer science , poisson–boltzmann equation , algorithm , statistical physics , mathematics , chemistry , mathematical optimization , physics , thermodynamics , quantum mechanics , artificial intelligence , organic chemistry , statistics , ion
Our goal is to develop accurate electrostatic models that can be implemented in current computational protein design protocols. To this end, we improve upon a previously reported pairwise decomposable, finite difference Poisson–Boltzmann (FDPB) model for protein design (Marshall et al., Protein Sci 2005, 14, 1293). The improvement involves placing generic sidechains at positions with unknown amino acid identity and explicitly capturing two‐body perturbations to the dielectric environment. We compare the original and improved FDPB methods to standard FDPB calculations in which the dielectric environment is completely determined by protein atoms. The generic sidechain approach yields a two to threefold increase in accuracy per residue or residue pair over the original pairwise FDPB implementation, with no additional computational cost. Distance dependent dielectric and solvent‐exclusion models were also compared with standard FDPB energies. The accuracy of the new pairwise FDPB method is shown to be superior to these models, even after reparameterization of the solvent‐exclusion model. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008