Transcription factor family-specific DNA shape readout revealed by quantitative specificity models

Lin Yang, Yaron Orenstein, Arttu Jolma, Yimeng Yin, Jussi Taipale, Ron Shamir*, Remo Rohs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

Abstract

Transcription factors (TFs) achieve DNA-binding specificity through contacts with functional groups of bases (base readout) and readout of structural properties of the double helix (shape readout). Currently, it remains unclear whether DNA shape readout is utilized by only a few selected TF families, or whether this mechanism is used extensively by most TF families. We resequenced data from previously published HT-SELEX experiments, the most extensive mammalian TF–DNA binding data available to date. Using these data, we demonstrated the contributions of DNA shape readout across diverse TF families and its importance in core motif-flanking regions. Statistical machine-learning models combined with feature-selection techniques helped to reveal the nucleotide position-dependent DNA shape readout in TF-binding sites and the TF family-specific position dependence. Based on these results, we proposed novel DNA shape logos to visualize the DNA shape preferences of TFs. Overall, this work suggests a way of obtaining mechanistic insights into TF–DNA binding without relying on experimentally solved all-atom structures.

Original languageEnglish
Article number910
JournalMolecular Systems Biology
Volume13
Issue number2
DOIs
StatePublished - 1 Feb 2017

Funding

FundersFunder number
Edmond J. Safra Center for Bioinformatics
Simons Institute for the Theory of Computing at UC Berkeley
National Science FoundationMCB-1413539
National Institutes of HealthU01GM103804
National Institute of General Medical SciencesR01GM106056
Alfred P. Sloan Foundation
Israel Science Foundation317/13
Knut och Alice Wallenbergs Stiftelse
Vetenskapsrådet
Tel Aviv University

    Keywords

    • DNA shape
    • binding specificity
    • feature selection
    • quantitative modeling
    • transcription factor

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