Literature DB >> 10760269

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

U Bastolla1, M Vendruscolo, E W Knapp.   

Abstract

We present a method for deriving energy functions for protein folding by maximizing the thermodynamic average of the overlap with the native state. The method has been tested by using the pairwise contact approximation of the energy function and generating alternative structures by threading sequences over a database of 1, 169 structures. With the derived energy function, most native structures: (i) have minimal energy and (ii) are thermodynamically rather stable, and (iii) the corresponding energy landscapes are smooth. Precisely, 92% of the 1,013 x-ray structures are stabilized. Most failures can be attributed to the neglect of interactions between chains forming polychain proteins and of interactions with cofactors. When these are considered, only nine cases remain unexplained. In contrast, 38% of NMR structures are not assigned properly.

Mesh:

Year:  2000        PMID: 10760269      PMCID: PMC18127          DOI: 10.1073/pnas.97.8.3977

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


  30 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.  Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes.

Authors:  M R Betancourt; D Thirumalai
Journal:  Protein Sci       Date:  1999-02       Impact factor: 6.725

3.  Simulations of the folding of a globular protein.

Authors:  J Skolnick; A Kolinski
Journal:  Science       Date:  1990-11-23       Impact factor: 47.728

4.  Protein folding mechanisms and the multidimensional folding funnel.

Authors:  N D Socci; J N Onuchic; P G Wolynes
Journal:  Proteins       Date:  1998-08-01

5.  Testing a new Monte Carlo algorithm for protein folding.

Authors:  U Bastolla; H Frauenkron; E Gerstner; P Grassberger; W Nadler
Journal:  Proteins       Date:  1998-07-01

6.  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

7.  Recovery of protein structure from contact maps.

Authors:  M Vendruscolo; E Kussell; E Domany
Journal:  Fold Des       Date:  1997

8.  On the thermodynamic hypothesis of protein folding.

Authors:  S Govindarajan; R A Goldstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-05-12       Impact factor: 11.205

9.  Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?

Authors:  J Skolnick; L Jaroszewski; A Kolinski; A Godzik
Journal:  Protein Sci       Date:  1997-03       Impact factor: 6.725

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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  13 in total

1.  Statistical properties of neutral evolution.

Authors:  Ugo Bastolla; Markus Porto; H Eduardo Roman; Michele Vendruscolo
Journal:  J Mol Evol       Date:  2003       Impact factor: 2.395

2.  Statistical potential for assessment and prediction of protein structures.

Authors:  Min-Yi Shen; Andrej Sali
Journal:  Protein Sci       Date:  2006-11       Impact factor: 6.725

3.  Statistical potential for modeling and ranking of protein-ligand interactions.

Authors:  Hao Fan; Dina Schneidman-Duhovny; John J Irwin; Guangqiang Dong; Brian K Shoichet; Andrej Sali
Journal:  J Chem Inf Model       Date:  2011-11-21       Impact factor: 4.956

4.  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

5.  A new parameter-rich structure-aware mechanistic model for amino acid substitution during evolution.

Authors:  Peter B Chi; Dohyup Kim; Jason K Lai; Nadia Bykova; Claudia C Weber; Jan Kubelka; David A Liberles
Journal:  Proteins       Date:  2017-12-12

6.  Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions.

Authors:  Shide Liang; Yaoqi Zhou; Nick Grishin; Daron M Standley
Journal:  J Comput Chem       Date:  2011-03-04       Impact factor: 3.376

7.  Reductive genome evolution in Buchnera aphidicola.

Authors:  Roeland C H J van Ham; Judith Kamerbeek; Carmen Palacios; Carolina Rausell; Federico Abascal; Ugo Bastolla; Jose M Fernández; Luis Jiménez; Marina Postigo; Francisco J Silva; Javier Tamames; Enrique Viguera; Amparo Latorre; Alfonso Valencia; Federico Morán; Andrés Moya
Journal:  Proc Natl Acad Sci U S A       Date:  2003-01-09       Impact factor: 11.205

8.  Why should we care about molecular coevolution?

Authors:  Francisco M Codoñer; Mario A Fares
Journal:  Evol Bioinform Online       Date:  2008-02-14       Impact factor: 1.625

9.  On simplified global nonlinear function for fitness landscape: a case study of inverse protein folding.

Authors:  Yun Xu; Changyu Hu; Yang Dai; Jie Liang
Journal:  PLoS One       Date:  2014-08-11       Impact factor: 3.240

Review 10.  Detecting selection on protein stability through statistical mechanical models of folding and evolution.

Authors:  Ugo Bastolla
Journal:  Biomolecules       Date:  2014-03-07
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