Ab initio prediction of transcription factor targets using structural knowledge

Tommy Kaplan*, Nir Friedman, Hanah Margalit

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

96 Scopus citations


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 DMA-recognition preferences are compatible with experimental results. We use these preferences to perform 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. Copyright:

Original languageAmerican English
Pages (from-to)5-13
Number of pages9
JournalPLoS Computational Biology
Issue number1
StatePublished - 2005


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