Literature DB >> 15802649

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

Shannon A Marshall1, Christina L Vizcarra, Stephen L Mayo.   

Abstract

Successfully modeling electrostatic interactions is one of the key factors required for the computational design of proteins with desired physical, chemical, and biological properties. In this paper, we present formulations of the finite difference Poisson-Boltzmann (FDPB) model that are pairwise decomposable by side chain. These methods use reduced representations of the protein structure based on the backbone and one or two side chains in order to approximate the dielectric environment in and around the protein. For the desolvation of polar side chains, the two-body model has a 0.64 kcal/mol RMSD compared to FDPB calculations performed using the full representation of the protein structure. Screened Coulombic interaction energies between side chains are approximated with an RMSD of 0.13 kcal/mol. The methods presented here are compatible with the computational demands of protein design calculations and produce energies that are very similar to the results of traditional FDPB calculations.

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Year:  2005        PMID: 15802649      PMCID: PMC2253281          DOI: 10.1110/ps.041259105

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  24 in total

Review 1.  Energy functions for protein design.

Authors:  D B Gordon; S A Marshall; S L Mayo
Journal:  Curr Opin Struct Biol       Date:  1999-08       Impact factor: 6.809

2.  Achieving stability and conformational specificity in designed proteins via binary patterning.

Authors:  S A Marshall; S L Mayo
Journal:  J Mol Biol       Date:  2001-01-19       Impact factor: 5.469

3.  Tanford-Kirkwood electrostatics for protein modeling.

Authors:  J J Havranek; P B Harbury
Journal:  Proc Natl Acad Sci U S A       Date:  1999-09-28       Impact factor: 11.205

4.  Improved modeling of side-chains in proteins with rotamer-based methods: a flexible rotamer model.

Authors:  J Mendes; A M Baptista; M A Carrondo; C M Soares
Journal:  Proteins       Date:  1999-12-01

Review 5.  Computational protein design.

Authors:  C M Kraemer-Pecore; A M Wollacott; J R Desjarlais
Journal:  Curr Opin Chem Biol       Date:  2001-12       Impact factor: 8.822

6.  Electrostatics significantly affect the stability of designed homeodomain variants.

Authors:  Shannon A Marshall; Chantal S Morgan; Stephen L Mayo
Journal:  J Mol Biol       Date:  2002-02-08       Impact factor: 5.469

7.  Automated design of specificity in molecular recognition.

Authors:  James J Havranek; Pehr B Harbury
Journal:  Nat Struct Biol       Date:  2003-01

Review 8.  Energy estimation in protein design.

Authors:  Joaquim Mendes; Raphael Guerois; Luis Serrano
Journal:  Curr Opin Struct Biol       Date:  2002-08       Impact factor: 6.809

9.  Role of main-chain electrostatics, hydrophobic effect and side-chain conformational entropy in determining the secondary structure of proteins.

Authors:  F Avbelj; L Fele
Journal:  J Mol Biol       Date:  1998-06-12       Impact factor: 5.469

10.  The dead-end elimination theorem and its use in protein side-chain positioning.

Authors:  J Desmet; M De Maeyer; B Hazes; I Lasters
Journal:  Nature       Date:  1992-04-09       Impact factor: 49.962

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  22 in total

1.  Dead-End Elimination with a Polarizable Force Field Repacks PCNA Structures.

Authors:  Stephen D LuCore; Jacob M Litman; Kyle T Powers; Shibo Gao; Ava M Lynn; William T A Tollefson; Timothy D Fenn; M Todd Washington; Michael J Schnieders
Journal:  Biophys J       Date:  2015-08-18       Impact factor: 4.033

2.  Polarizable Atomic Multipole Solutes in a Generalized Kirkwood Continuum.

Authors:  Michael J Schnieders; Jay W Ponder
Journal:  J Chem Theory Comput       Date:  2007-11       Impact factor: 6.006

3.  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

4.  CIRSE: a solvation energy estimator compatible with flexible protein docking and design applications.

Authors:  David S Cerutti; Tushar Jain; J Andrew McCammon
Journal:  Protein Sci       Date:  2006-07       Impact factor: 6.725

5.  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

6.  Accurate, conformation-dependent predictions of solvent effects on protein ionization constants.

Authors:  P Barth; T Alber; P B Harbury
Journal:  Proc Natl Acad Sci U S A       Date:  2007-03-14       Impact factor: 11.205

Review 7.  Progress in computational protein design.

Authors:  Shaun M Lippow; Bruce Tidor
Journal:  Curr Opin Biotechnol       Date:  2007-07-20       Impact factor: 9.740

Review 8.  Computer-aided design of functional protein interactions.

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

9.  Adapting Poisson-Boltzmann to the self-consistent mean field theory: application to protein side-chain modeling.

Authors:  Patrice Koehl; Henri Orland; Marc Delarue
Journal:  J Chem Phys       Date:  2011-08-07       Impact factor: 3.488

10.  A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins.

Authors:  Li Xiao; Jianxiong Diao; D'Artagnan Greene; Junmei Wang; Ray Luo
Journal:  J Chem Theory Comput       Date:  2017-06-14       Impact factor: 6.006

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