Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

Abstract

<div><p>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 use these preferences to perform a genome-wide scan for direct targets of <em>Drosophila melanogaster</em> Cys₂His₂ transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.</p> </div