Transcription factor family-specific DNA shape readout revealed by quantitative specificity models.
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.
Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA, USA.
Lin
L
Yang
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Yaron
Y
Orenstein
Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Arttu
A
Jolma
Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Yimeng
Y
Yin
Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
Jussi
J
Taipale
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel rshamir@tau.ac.il rohs@usc.edu.
Ron
0000-0003-1889-9870
R
Shamir
Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA, USA rshamir@tau.ac.il rohs@usc.edu.
Remo
0000-0003-1752-1884
R
Rohs
D000818:Animals; D001665:Binding Sites; D004247:DNA / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D006801:Humans; D000069550:Machine Learning; D008322:Mammals / Q000235:genetics; D051379:Mice; D009690:Nucleic Acid Conformation; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
D014157:Transcription Factors; D004247:DNA
DNA shape; binding specificity; feature selection; quantitative modeling; transcription factor