Nucleic Acids Research Advance Access originally published online on January 30, 2007
Nucleic Acids Research 2007 35(4):1085-1097; doi:10.1093/nar/gkl1155
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Nucleic Acids Research, 2007, Vol. 35, No. 4 1085-1097
© 2007 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Structure-based prediction of C2H2 zinc-finger binding specificity: sensitivity to docking geometry
Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, USA
*To whom correspondence should be addressed. Tel: + 1 212 851 4651; Fax: + 1 212 8514 650; Email: bh6{at}columbia.edu
Received November 8, 2006. Revised December 13, 2006. Accepted December 14, 2006.
Predicting the binding specificity of transcription factors is a critical step in the characterization and computational identification and of cis-regulatory elements in genomic sequences. Here we use protein–DNA structures to predict binding specificity and consider the possibility of predicting position weight matrices (PWM) for an entire protein family based on the structures of just a few family members. A particular focus is the sensitivity of prediction accuracy to the docking geometry of the structure used. We investigate this issue with the goal of determining how similar two docking geometries must be for binding specificity predictions to be accurate. Docking similarity is quantified using our recently described interface alignment score (IAS). Using a molecular-mechanics force field, we predict high-affinity nucleotide sequences that bind to the second zinc-finger (ZF) domain from the Zif268 protein, using different C2H2 ZF domains as structural templates. We identify a strong relationship between IAS values and prediction accuracy, and define a range of IAS values for which accurate structure-based predictions of binding specificity is to be expected. The implication of our results for large-scale, structure-based prediction of PWMs is discussed.
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