Kaplan by nuhman10


									ab initio prediction of transcription factor targets using
structural knowledge

Tommy Kaplan

Current approaches for identification and detection of transcription factor binding sites
rely on an extensive set of known target genes. Here we describe a novel structure-based
approach applicable to transcription factors with no prior binding data. Our approach
combines sequence data and structural information to infer context-specific amino acid-
nucleotide recognition preferences. These are used to predict binding sites for novel
transcription factors from the same structural family. We demonstrate our approach on
the Cys2His2 Zinc Finger protein family, and show that the learned DNA-recognition
preferences are compatible with experimental results. We apply these preferences in a
genome-wide scan for direct targets of Drosophila melanogaster Cys2His2 transcription
factors. By analyzing the predicted targets along with gene annotation and expression
data we infer the function and activity of these proteins.

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