Literature DB >> 8515455

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

F Eisenmenger1, P Argos, R Abagyan.   

Abstract

Protein homology modelling typically involves the prediction of side-chain conformations in the modelled protein while assuming a main-chain trace taken from a known tertiary structure of a protein with homologous sequence. It is generally believed that the need to examine all possible combinations of side-chain conformations poses the major obstacle to accurate homology modelling. Methods proposed heretofore use only discrete or limited searches of the side-chain torsion angle space to mitigate the combinatorial problem and also rely on simplified energy functions for calculational speed. The configurational constraints are typically based upon use of frequently observed torsion angles, fixed steps in torsion angles, or oligopeptide segments taken from tertiary structural databanks that are similar in sequence and conformation with the target structure. In the present work, a more fundamental approach is explored for several protein structures and it is demonstrated that the combinatorial barrier in side-chain placement hardly exists. Each side-group can be configured individually in the environment of only the backbone atoms using a systematic search procedure combined with extensive local energy minimization. Tests, using the main-chain or both the main-chain and remaining side-chain atoms to calculate low energy geometries for each residue, established the dominance of the main-chain contribution. The final structure is achieved by combining the individually placed side-chains followed by a full energy refinement of the structure. The prediction accuracy of the present homology modelling technique was assessed relative to other automated procedures and was found to yield improved predictions relative to the known side-chain conformations determined by X-ray crystallography.

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Year:  1993        PMID: 8515455     DOI: 10.1006/jmbi.1993.1331

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


  19 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.  A stochastic algorithm for global optimization and for best populations: a test case of side chains in proteins.

Authors:  Meir Glick; Anwar Rayan; Amiram Goldblum
Journal:  Proc Natl Acad Sci U S A       Date:  2002-01-15       Impact factor: 11.205

3.  Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library.

Authors:  Ronald W Peterson; P Leslie Dutton; A Joshua Wand
Journal:  Protein Sci       Date:  2004-03       Impact factor: 6.725

4.  The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactions.

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Journal:  Protein Sci       Date:  2007-01-22       Impact factor: 6.725

Review 5.  Flexible ligand docking to multiple receptor conformations: a practical alternative.

Authors:  Maxim Totrov; Ruben Abagyan
Journal:  Curr Opin Struct Biol       Date:  2008-02-25       Impact factor: 6.809

6.  Modeling mutations in protein structures.

Authors:  Eric Feyfant; Andrej Sali; András Fiser
Journal:  Protein Sci       Date:  2007-09       Impact factor: 6.725

7.  Simultaneous modeling of multiple loops in proteins.

Authors:  D Rosenbach; R Rosenfeld
Journal:  Protein Sci       Date:  1995-03       Impact factor: 6.725

8.  Does conformational free energy distinguish loop conformations in proteins?

Authors:  J L Pellequer; S W Chen
Journal:  Biophys J       Date:  1997-11       Impact factor: 4.033

9.  Cavities and packing at protein interfaces.

Authors:  S J Hubbard; P Argos
Journal:  Protein Sci       Date:  1994-12       Impact factor: 6.725

10.  A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE).

Authors:  Giovanni Bottegoni; Irina Kufareva; Maxim Totrov; Ruben Abagyan
Journal:  J Comput Aided Mol Des       Date:  2008-02-14       Impact factor: 3.686

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