Literature DB >> 8563626

The use of side-chain packing methods in modeling bacteriophage repressor and cro proteins.

S Y Chung1, S Subbiah.   

Abstract

In recent years, it has been repeatedly demonstrated that the coordinates of the main-chain atoms alone are sufficient to determine the side-chain conformations of buried residues of compact proteins. Given a perfect backbone, the side-chain packing method can predict the side-chain conformations to an accuracy as high as 1.2 A RMS deviation (RMSD) with greater than 80% of the chi angles correct. However, similarly rigorous studies have not been conducted to determine how well these apply, if at all, to the more important problem of homology modeling per se. Specifically, if the available backbone is imperfect, as expected for practical application of homology modeling, can packing constraints alone achieve sufficiently accurate predictions to be useful? Here, by systematically applying such methods to the pairwise modeling of two repressor and two cro proteins from the closely related bacteriophages 434 and P22, we find that when the backbone RMSD is 0.8 A, the prediction on buried side chain is accurate with an RMS error of 1.8 A and approximately 70% of the chi angles correctly predicted. When the backbone RMSD is larger, in the range of 1.6-1.8 A, the prediction quality is still significantly better than random, with RMS error at 2.2 A on the buried side chains and 60% accuracy on chi angles. Together these results suggest the following rules-of-thumb for homology modeling of buried side chains. When the sequence identity between the modeled sequence and the template sequence is > 50% (or, equivalently, the expected backbone RMSD is < 1 A), side-chain packing methods work well. When sequence identity is between 30-50%, reflecting a backbone RMS error of 1-2 A, it is still valid to use side-chain packing methods to predict the buried residues, albeit with care. When sequence identity is below 30% (or backbone RMS error greater than 2 A), the backbone constraint alone is unlikely to produce useful models. Other methods, such as those involving the use of database fragments to reconstruct a template backbone, may be necessary as a complementary guide for modeling.

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Year:  1995        PMID: 8563626      PMCID: PMC2143027          DOI: 10.1002/pro.5560041107

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  23 in total

1.  Database algorithm for generating protein backbone and side-chain co-ordinates from a C alpha trace application to model building and detection of co-ordinate errors.

Authors:  L Holm; C Sander
Journal:  J Mol Biol       Date:  1991-03-05       Impact factor: 5.469

2.  Comparative modeling of homologous proteins.

Authors:  J Greer
Journal:  Methods Enzymol       Date:  1991       Impact factor: 1.600

3.  A database of protein structure families with common folding motifs.

Authors:  L Holm; C Ouzounis; C Sander; G Tuparev; G Vriend
Journal:  Protein Sci       Date:  1992-12       Impact factor: 6.725

4.  Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes.

Authors:  J W Ponder; F M Richards
Journal:  J Mol Biol       Date:  1987-02-20       Impact factor: 5.469

Review 5.  Knowledge-based prediction of protein structures and the design of novel molecules.

Authors:  T L Blundell; B L Sibanda; M J Sternberg; J M Thornton
Journal:  Nature       Date:  1987 Mar 26-Apr 1       Impact factor: 49.962

6.  Modeling side-chain conformation for homologous proteins using an energy-based rotamer search.

Authors:  C Wilson; L M Gregoret; D A Agard
Journal:  J Mol Biol       Date:  1993-02-20       Impact factor: 5.469

7.  Backbone-dependent rotamer library for proteins. Application to side-chain prediction.

Authors:  R L Dunbrack; M Karplus
Journal:  J Mol Biol       Date:  1993-03-20       Impact factor: 5.469

8.  A method to configure protein side-chains from the main-chain trace in homology modelling.

Authors:  F Eisenmenger; P Argos; R Abagyan
Journal:  J Mol Biol       Date:  1993-06-05       Impact factor: 5.469

9.  Predicting protein mutant energetics by self-consistent ensemble optimization.

Authors:  C Lee
Journal:  J Mol Biol       Date:  1994-02-25       Impact factor: 5.469

10.  Using known substructures in protein model building and crystallography.

Authors:  T A Jones; S Thirup
Journal:  EMBO J       Date:  1986-04       Impact factor: 11.598

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  3 in total

1.  The reconstruction of atomic co-ordinates from a protein stereo ribbon diagram when additional information for sufficient sidechain positions is available.

Authors:  P S de Oliveira; R C Garratt
Journal:  J Comput Aided Mol Des       Date:  1998-11       Impact factor: 3.686

2.  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

Review 3.  Rigorous performance evaluation in protein structure modelling and implications for computational biology.

Authors:  John Moult
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-03-29       Impact factor: 6.237

  3 in total

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