Volume 33, Issue 4 pp. 535-549
Research Article

Modelling repressor proteins docking to DNA

Patrick Aloy

Patrick Aloy

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, United Kingdom

Institut de Biologia Fonamental and Departament de Bioquímica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

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Gidon Moont

Gidon Moont

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, United Kingdom

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Henry A. Gabb

Henry A. Gabb

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, United Kingdom

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Enrique Querol

Enrique Querol

Institut de Biologia Fonamental and Departament de Bioquímica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

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Francesc X. Aviles

Francesc X. Aviles

Institut de Biologia Fonamental and Departament de Bioquímica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

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Michael J.E. Sternberg

Corresponding Author

Michael J.E. Sternberg

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, United Kingdom

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, 44 Lincoln's Inn Fields, London WC2A 3PX, United Kingdom.===Search for more papers by this author

Abstract

The docking of repressor proteins to DNA starting from the unbound protein and model-built DNA coordinates is modeled computationally. The approach was evaluated on eight repressor/DNA complexes that employed different modes for protein/ DNA recognition. The global search is based on a protein-protein docking algorithm that evaluates shape and electrostatic complementarity, which was modified to consider the importance of electrostatic features in DNA-protein recognition. Complexes were then ranked by an empirical score for the observed amino acid /nucleotide pairings (i.e., protein-DNA pair potentials) derived from a database of 20 protein/DNA complexes. A good prediction had at least 65% of the correct contacts modeled. This approach was able to identify a good solution at rank four or better for three out of the eight complexes. Predicted complexes were filtered by a distance constraint based on experimental data defining the DNA footprint. This improved coverage to four out of eight complexes having a good model at rank four or better. The additional use of amino acid mutagenesis and phylogenetic data defining residues on the repressor resulted in between 2 and 27 models that would have to be examined to find a good solution for seven of the eight test systems. This study shows that starting with unbound coordinates one can predict three-dimensional models for protein/DNA complexes that do not involve gross conformational changes on association. Proteins 33:535–549, 1998. © 1998 Wiley-Liss, Inc.

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