Literature DB >> 9876919

Determinants of side chain conformational preferences in protein structures.

R Samudrala1, J Moult.   

Abstract

A discriminatory function based on a statistical analysis of atomic contacts in protein structures is used for selecting side chain rotamers given a peptide main chain. The function allows us to rank different possible side chain conformations on the basis of contacts between side chain atoms and atoms in the environment. We compare the differences in constructing side chain conformations using contacts with only the local main chain, using the entire main chain, and by building pairs of side chains simultaneously with local main chain information. Using only the local main chain allows us to construct side chains with approximately 75% of the chi1 angles within 30 degrees of the experimental value, and an average side chain atom r.m.s.d. of 1.72 A in a set of 10 proteins. The results of constructing side chains for the 10 proteins are compared with the results of other side chain building methods previously published. The comparison shows similar accuracies. An advantage of the present method is that it can be used to select a small number of likely side chain conformations for each residue, thus permitting limited combinatorial searches for building multiple protein side chains simultaneously.

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Year:  1998        PMID: 9876919     DOI: 10.1093/protein/11.11.991

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  11 in total

1.  Side-chain modeling with an optimized scoring function.

Authors:  Shide Liang; Nick V Grishin
Journal:  Protein Sci       Date:  2002-02       Impact factor: 6.725

2.  PROTINFO: Secondary and tertiary protein structure prediction.

Authors:  Ling-Hong Hung; Ram Samudrala
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

3.  A graph-theory algorithm for rapid protein side-chain prediction.

Authors:  Adrian A Canutescu; Andrew A Shelenkov; Roland L Dunbrack
Journal:  Protein Sci       Date:  2003-09       Impact factor: 6.725

4.  GEM: a Gaussian Evolutionary Method for predicting protein side-chain conformations.

Authors:  Jinn-Moon Yang; Chi-Hung Tsai; Ming-Jing Hwang; Huai-Kuang Tsai; Jenn-Kang Hwang; Cheng-Yan Kao
Journal:  Protein Sci       Date:  2002-08       Impact factor: 6.725

Review 5.  Advances in homology protein structure modeling.

Authors:  Zhexin Xiang
Journal:  Curr Protein Pept Sci       Date:  2006-06       Impact factor: 3.272

6.  IRECS: a new algorithm for the selection of most probable ensembles of side-chain conformations in protein models.

Authors:  Christoph Hartmann; Iris Antes; Thomas Lengauer
Journal:  Protein Sci       Date:  2007-06-13       Impact factor: 6.725

7.  Fine grained sampling of residue characteristics using molecular dynamics simulation.

Authors:  Hyun Joo; Xiaotao Qu; Rosemarie Swanson; C Michael McCallum; Jerry Tsai
Journal:  Comput Biol Chem       Date:  2010-06-19       Impact factor: 2.877

8.  An estimate of the numbers and density of low-energy structures (or decoys) in the conformational landscape of proteins.

Authors:  Kanagasabai Vadivel; Gautham Namasivayam
Journal:  PLoS One       Date:  2009-04-09       Impact factor: 3.240

9.  PROTINFO: new algorithms for enhanced protein structure predictions.

Authors:  Ling-Hong Hung; Shing-Chung Ngan; Tianyun Liu; Ram Samudrala
Journal:  Nucleic Acids Res       Date:  2005-07-01       Impact factor: 16.971

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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