Literature DB >> 16126228

An atomic environment potential for use in protein structure prediction.

Christopher M Summa1, Michael Levitt, William F Degrado.   

Abstract

We describe the derivation and testing of a knowledge-based atomic environment potential for the modeling of protein structural energetics. An analysis of the probabilities of atomic interactions in a dataset of high-resolution protein structures shows that the probabilities of non-bonded inter-atomic contacts are not statistically independent events, and that the multi-body contact frequencies are poorly predicted from pairwise contact potentials. A pseudo-energy function is defined that measures the preferences for protein atoms to be in a given microenvironment defined by the number of contacting atoms in the environment and its atomic composition. This functional form is tested for its ability to recognize native protein structures amongst an ensemble of decoy structures and a detailed relative performance comparison is made with a number of common functions used in protein structure prediction.

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Year:  2005        PMID: 16126228     DOI: 10.1016/j.jmb.2005.07.054

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  26 in total

1.  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

2.  LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading.

Authors:  Vinay Pulim; Jadwiga Bienkowska; Bonnie Berger
Journal:  Protein Sci       Date:  2007-12-20       Impact factor: 6.725

3.  Near-native structure refinement using in vacuo energy minimization.

Authors:  Christopher M Summa; Michael Levitt
Journal:  Proc Natl Acad Sci U S A       Date:  2007-02-20       Impact factor: 11.205

4.  A new generation of statistical potentials for proteins.

Authors:  Y Dehouck; D Gilis; M Rooman
Journal:  Biophys J       Date:  2006-03-13       Impact factor: 4.033

5.  Local quality assessment in homology models using statistical potentials and support vector machines.

Authors:  Marc Fasnacht; Jiang Zhu; Barry Honig
Journal:  Protein Sci       Date:  2007-06-28       Impact factor: 6.725

6.  Development of a physics-based force field for the scoring and refinement of protein models.

Authors:  Liliana Wroblewska; Anna Jagielska; Jeffrey Skolnick
Journal:  Biophys J       Date:  2008-01-04       Impact factor: 4.033

Review 7.  Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods.

Authors:  T Scott Chen; Amy E Keating
Journal:  Protein Sci       Date:  2012-06-08       Impact factor: 6.725

8.  Optimized atomic statistical potentials: assessment of protein interfaces and loops.

Authors:  Guang Qiang Dong; Hao Fan; Dina Schneidman-Duhovny; Ben Webb; Andrej Sali
Journal:  Bioinformatics       Date:  2013-09-27       Impact factor: 6.937

9.  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

10.  Four distances between pairs of amino acids provide a precise description of their interaction.

Authors:  Mati Cohen; Vladimir Potapov; Gideon Schreiber
Journal:  PLoS Comput Biol       Date:  2009-08-14       Impact factor: 4.475

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