Literature DB >> 8643516

How optimization of potential functions affects protein folding.

M H Hao1, H A Scheraga.   

Abstract

The relationship between the optimization of the potential function and the foldability of theoretical protein models is studied based on investigations of a 27-mer cubic-lattice protein model and a more realistic lattice model for the protein crambin. In both the simple and the more complicated systems, optimization of the energy parameters achieves significant improvements in the statistical-mechanical characteristics of the systems and leads to foldable protein models in simulation experiments. The foldability of the protein models is characterized by their statistical-mechanical properties--e.g., by the density of states and by Monte Carlo folding simulations of the models. With optimized energy parameters, a high level of consistency exists among different interactions in the native structures of the protein models, as revealed by a correlation function between the optimized energy parameters and the native structure of the model proteins. The results of this work are relevant to the design of a general potential function for folding proteins by theoretical simulations.

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Year:  1996        PMID: 8643516      PMCID: PMC39392          DOI: 10.1073/pnas.93.10.4984

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  23 in total

1.  Contact potential that recognizes the correct folding of globular proteins.

Authors:  V N Maiorov; G M Crippen
Journal:  J Mol Biol       Date:  1992-10-05       Impact factor: 5.469

2.  Protein folding funnels: a kinetic approach to the sequence-structure relationship.

Authors:  P E Leopold; M Montal; J N Onuchic
Journal:  Proc Natl Acad Sci U S A       Date:  1992-09-15       Impact factor: 11.205

3.  New Monte Carlo algorithm: Entropic sampling.

Authors: 
Journal:  Phys Rev Lett       Date:  1993-07-12       Impact factor: 9.161

4.  Proteins with selected sequences fold into unique native conformation.

Authors: 
Journal:  Phys Rev Lett       Date:  1994-06-13       Impact factor: 9.161

5.  New Monte Carlo technique for studying phase transitions.

Authors: 
Journal:  Phys Rev Lett       Date:  1988-12-05       Impact factor: 9.161

6.  Highly ordered crystals of the plant seed protein crambin.

Authors:  M M Teeter; W A Hendrickson
Journal:  J Mol Biol       Date:  1979-01-15       Impact factor: 5.469

7.  Energy functions for peptides and proteins. I. Derivation of a consistent force field including the hydrogen bond from amide crystals.

Authors:  A T Hagler; E Huler; S Lifson
Journal:  J Am Chem Soc       Date:  1974-08-21       Impact factor: 15.419

8.  How does a protein fold?

Authors:  A Sali; E Shakhnovich; M Karplus
Journal:  Nature       Date:  1994-05-19       Impact factor: 49.962

9.  A new approach to the design of stable proteins.

Authors:  E I Shakhnovich; A M Gutin
Journal:  Protein Eng       Date:  1993-11

10.  Spin glasses and the statistical mechanics of protein folding.

Authors:  J D Bryngelson; P G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  1987-11       Impact factor: 11.205

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  12 in total

1.  A statistical mechanical method to optimize energy functions for protein folding.

Authors:  U Bastolla; M Vendruscolo; E W Knapp
Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-11       Impact factor: 11.205

2.  Scoring functions in protein folding and design.

Authors:  R I Dima; J R Banavar; A Maritan
Journal:  Protein Sci       Date:  2000-04       Impact factor: 6.725

3.  A method for parameter optimization in computational biology.

Authors:  J B Rosen; A T Phillips; S Y Oh; K A Dill
Journal:  Biophys J       Date:  2000-12       Impact factor: 4.033

4.  A structure-based method for derivation of all-atom potentials for protein folding.

Authors:  Edo Kussell; Jun Shimada; Eugene I Shakhnovich
Journal:  Proc Natl Acad Sci U S A       Date:  2002-04-09       Impact factor: 11.205

5.  Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K.

Authors:  J Lee; A Liwo; H A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  1999-03-02       Impact factor: 11.205

6.  Recovering physical potentials from a model protein databank.

Authors:  J W Mullinax; W G Noid
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-01       Impact factor: 11.205

7.  Protein structure prediction by global optimization of a potential energy function.

Authors:  A Liwo; J Lee; D R Ripoll; J Pillardy; H A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  1999-05-11       Impact factor: 11.205

8.  Self-consistently optimized energy functions for protein structure prediction by molecular dynamics.

Authors:  K K Koretke; Z Luthey-Schulten; P G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  1998-03-17       Impact factor: 11.205

9.  Protein fold recognition without Boltzmann statistics or explicit physical basis.

Authors:  T Huber; A E Torda
Journal:  Protein Sci       Date:  1998-01       Impact factor: 6.725

10.  Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model.

Authors:  M Vendruscolo
Journal:  J Biol Phys       Date:  2001-06       Impact factor: 1.365

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