An improved pairwise decomposable finite-difference Poisson-Boltzmann method for computational protein design

Christina L. Vizcarra, Naigong Zhang, Shannon A. Marshall, Ned S. Wingreen, Chen Zeng, Stephen L. Mayo

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1153-1162
Number of pages10
JournalJournal of Computational Chemistry
Volume29
Issue number7
DOIs
StatePublished - May 1 2008

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Computational Mathematics

Keywords

  • Continuum solvation
  • Electrostatics
  • Poisson-Boltzmann
  • Protein design

Fingerprint Dive into the research topics of 'An improved pairwise decomposable finite-difference Poisson-Boltzmann method for computational protein design'. Together they form a unique fingerprint.

Cite this