Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

Abstract

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 Cys ₂His ₂ 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 Drosophila melanogaster Cys ₂His ₂ transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.

Synopsis

Cells respond to dynamic changes in their environment by invoking various cellular processes, coordinated by a complex regulatory program. A main component of this program is the regulation of transcription, which is mainly accomplished by transcription factors that bind the DNA in the vicinity of genes. To better understand transcriptional regulation, advanced computational approaches are needed for linking between transcription factors and their targets. The authors describe a novel approach by which the binding site of a given transcription factor can be characterized without previous experimental binding data. This approach involves learning a set of context-specific amino acid–nucleotide recognition preferences that, when combined with the sequence and structure of the protein, can predict its specific binding preferences. Applying this approach to the Cys ₂His ₂ Zinc Finger protein family demonstrated its genome-wide potential by automatically predicting the direct targets of 29 regulators in the genome of the fruit fly Drosophila melanogaster. At present, with the availability of many genome sequences, there are numerous proteins annotated as transcription factors based on their sequence alone. This approach offers a promising direction for revealing the targets of these factors and for understanding their roles in the cellular network.