Predicting transcription factor binding sites using structural knowledge

Tommy Kaplan, Nir Friedman*, Hanah Margalit

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

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 apply our approach to the Cys 2His2 Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with various experimental results. To demonstrate the potential of our algorithm, we use the learned preferences to predict binding site models for novel proteins from the same family. These models are then used in genomic scans to find putative binding sites of the novel proteins.

Original languageAmerican English
Pages (from-to)522-537
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3500
DOIs
StatePublished - 2005
Event9th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2005 - Cambridge, MA, United States
Duration: 14 May 200518 May 2005

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