Quantitative modeling of transcription factor binding specificities using DNA shape
Tianyin Zhoua,1, Ning Shenb,c,1, Lin Yanga, Namiko Abed, John Hortonc,e, Richard S. Mannd,f, Harmen J. Bussemakerf,g, Raluca Gordânc,e,2, and Remo Rohsa,2
aMolecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089;
Departments of bPharmacology and Cancer Biology and
eBiostatistics and Bioinformatics and
cCenter for Genomic and Computational Biology, Duke University, Durham, NC 27708;
Departments of dBiochemistry and Molecular Biophysics and
fSystems Biology, Columbia University, New York, NY 10032; and
gDepartment of Biological Sciences, Columbia University, New York, NY 10027
Edited by Steven Henikoff, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved February 13, 2015 (received for review November 18, 2014)
Genomes provide an abundance of putative binding sites for each transcription factor (TF). However, only small subsets of these potential targets are functional. TFs of the same protein family bind to target sites that are very similar but not identical. This distinction allows closely related TFs to regulate different genes and thus execute distinct functions. Because the nucleotide sequence of the core motif is often not sufficient for identifying a genomic target, we refined the description of TF binding sites by introducing a combination of DNA sequence and shape features, which consistently improved the modeling of in vitro TF−DNA binding specificities. Although additional factors affect TF binding in vivo, shape-augmented models reveal binding specificity mechanisms that are not apparent from sequence alone.
DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF−DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.
protein−DNA recognition statistical machine learning support vector regression protein binding microarray DNA structure
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