Literature DB >> 19384995

Identifying native-like protein structures with scoring functions based on all-atom ECEPP force fields, implicit solvent models and structure relaxation.

Yelena A Arnautova1, Yury N Vorobjev, Jorge A Vila, Harold A Scheraga.   

Abstract

Availability of energy functions which can discriminate native-like from non-native protein conformations is crucial for theoretical protein structure prediction and refinement of low-resolution protein models. This article reports the results of benchmark tests for scoring functions based on two all-atom ECEPP force fields, that is, ECEPP/3 and ECEPP05, and two implicit solvent models for a large set of protein decoys. The following three scoring functions are considered: (i) ECEPP05 plus a solvent-accessible surface area model with the parameters optimized with a set of protein decoys (ECEPP05/SA); (ii) ECEPP/3 plus the solvent-accessible surface area model of Ooi et al. (Proc Natl Acad Sci USA 1987;84:3086-3090) (ECEPP3/OONS); and (iii) ECEPP05 plus an implicit solvent model based on a solution of the Poisson equation with an optimized Fast Adaptive Multigrid Boundary Element (FAMBEpH) method (ECEPP05/FAMBEpH). Short Monte Carlo-with-Minimization (MCM) simulations, following local energy minimization, are used as a scoring method with ECEPP05/SA and ECEPP3/OONS potentials, whereas energy calculation is used with ECEPP05/FAMBEpH. The performance of each scoring function is evaluated by examining its ability to distinguish between native-like and non-native protein structures. The results of the tests show that the new ECEPP05/SA scoring function represents a significant improvement over the earlier ECEPP3/OONS version of the force field. Thus, it is able to rank native-like structures with C(alpha) root-mean-square-deviations below 3.5 A as lowest-energy conformations for 76% and within the top 10 for 87% of the proteins tested, compared with 69 and 80%, respectively, for ECEPP3/OONS. The use of the FAMBEpH solvation model, which provides a more accurate description of the protein-solvent interactions, improves the discriminative ability of the scoring function to 89%. All failed tests in which the native-like structures cannot be discriminated as those with low energy, are due to omission of protein-protein interactions. The results of this study represent a benchmark in force-field development, and may be useful for evaluation of the performance of different force fields.

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Year:  2009        PMID: 19384995      PMCID: PMC4502597          DOI: 10.1002/prot.22414

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  41 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Identifying native-like protein structures using physics-based potentials.

Authors:  Brian N Dominy; Charles L Brooks
Journal:  J Comput Chem       Date:  2002-01-15       Impact factor: 3.376

3.  Discrimination of the native from misfolded protein models with an energy function including implicit solvation.

Authors:  T Lazaridis; M Karplus
Journal:  J Mol Biol       Date:  1999-05-07       Impact factor: 5.469

4.  Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the Surface Generalized Born solvent model.

Authors:  Anthony K Felts; Emilio Gallicchio; Anders Wallqvist; Ronald M Levy
Journal:  Proteins       Date:  2002-08-01

5.  A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations.

Authors:  Yong Duan; Chun Wu; Shibasish Chowdhury; Mathew C Lee; Guoming Xiong; Wei Zhang; Rong Yang; Piotr Cieplak; Ray Luo; Taisung Lee; James Caldwell; Junmei Wang; Peter Kollman
Journal:  J Comput Chem       Date:  2003-12       Impact factor: 3.376

6.  Physical scoring function based on AMBER force field and Poisson-Boltzmann implicit solvent for protein structure prediction.

Authors:  Meng-Juei Hsieh; Ray Luo
Journal:  Proteins       Date:  2004-08-15

7.  Toward high-resolution de novo structure prediction for small proteins.

Authors:  Philip Bradley; Kira M S Misura; David Baker
Journal:  Science       Date:  2005-09-16       Impact factor: 47.728

8.  An all-atom force field for tertiary structure prediction of helical proteins.

Authors:  T Herges; W Wenzel
Journal:  Biophys J       Date:  2004-11       Impact factor: 4.033

9.  On the multiple-minima problem in the conformational analysis of polypeptides. II. An electrostatically driven Monte Carlo method--tests on poly(L-alanine).

Authors:  D R Ripoll; H A Scheraga
Journal:  Biopolymers       Date:  1988-08       Impact factor: 2.505

10.  FAMBE-pH: a fast and accurate method to compute the total solvation free energies of proteins.

Authors:  Yury N Vorobjev; Jorge A Vila; Harold A Scheraga
Journal:  J Phys Chem B       Date:  2008-08-07       Impact factor: 2.991

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  11 in total

1.  Further evidence for the likely completeness of the library of solved single domain protein structures.

Authors:  Jeffrey Skolnick; Hongyi Zhou; Michal Brylinski
Journal:  J Phys Chem B       Date:  2012-02-13       Impact factor: 2.991

2.  Selecting high quality protein structures from diverse conformational ensembles.

Authors:  Ashwin Subramani; Peter A DiMaggio; Christodoulos A Floudas
Journal:  Biophys J       Date:  2009-09-16       Impact factor: 4.033

3.  GneimoSim: a modular internal coordinates molecular dynamics simulation package.

Authors:  Adrien B Larsen; Jeffrey R Wagner; Saugat Kandel; Romelia Salomon-Ferrer; Nagarajan Vaidehi; Abhinandan Jain
Journal:  J Comput Chem       Date:  2014-09-27       Impact factor: 3.376

4.  Coupled molecular dynamics and continuum electrostatic method to compute the ionization pKa's of proteins as a function of pH. Test on a large set of proteins.

Authors:  Yury N Vorobjev; Harold A Scheraga; Jorge A Vila
Journal:  J Biomol Struct Dyn       Date:  2017-02-24

5.  Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function.

Authors:  Michal Brylinski; Mu Gao; Jeffrey Skolnick
Journal:  Phys Chem Chem Phys       Date:  2011-06-08       Impact factor: 3.676

6.  Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling.

Authors:  Yelena A Arnautova; Ruben A Abagyan; Maxim Totrov
Journal:  Proteins       Date:  2011-02

Review 7.  Molecular mechanics.

Authors:  Kenno Vanommeslaeghe; Olgun Guvench; Alexander D MacKerell
Journal:  Curr Pharm Des       Date:  2014       Impact factor: 3.116

Review 8.  Design and application of implicit solvent models in biomolecular simulations.

Authors:  Jens Kleinjung; Franca Fraternali
Journal:  Curr Opin Struct Biol       Date:  2014-05-20       Impact factor: 6.809

9.  Internal coordinate molecular dynamics: a foundation for multiscale dynamics.

Authors:  Nagarajan Vaidehi; Abhinandan Jain
Journal:  J Phys Chem B       Date:  2015-01-06       Impact factor: 2.991

10.  Implicit Solvation Parameters Derived from Explicit Water Forces in Large-Scale Molecular Dynamics Simulations.

Authors:  Jens Kleinjung; Walter R P Scott; Jane R Allison; Wilfred F van Gunsteren; Franca Fraternali
Journal:  J Chem Theory Comput       Date:  2012-06-12       Impact factor: 6.006

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