title: Quantitative modeling of transcription factor binding specificities using DNA shape.
abstract: 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.
authors.affiliation: Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089;
authors.forename: Tianyin
authors.initials: T
authors.lastname: Zhou
authors.affiliation: Departments of Pharmacology and Cancer Biology and Center for Genomic and Computational Biology, Duke University, Durham, NC 27708;
authors.forename: Ning
authors.initials: N
authors.lastname: Shen
authors.affiliation: Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089;
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.affiliation: Departments of Biochemistry and Molecular Biophysics and.
authors.forename: Namiko
authors.initials: N
authors.lastname: Abe
authors.affiliation: Center for Genomic and Computational Biology, Duke University, Durham, NC 27708; Biostatistics and Bioinformatics and.
authors.forename: John
authors.initials: J
authors.lastname: Horton
authors.affiliation: Departments of Biochemistry and Molecular Biophysics and Systems Biology, Columbia University, New York, NY 10032; and.
authors.forename: Richard S
authors.initials: RS
authors.lastname: Mann
authors.affiliation: Systems Biology, Columbia University, New York, NY 10032; and Department of Biological Sciences, Columbia University, New York, NY 10027.
authors.forename: Harmen J
authors.initials: HJ
authors.lastname: Bussemaker
authors.affiliation: Center for Genomic and Computational Biology, Duke University, Durham, NC 27708; Biostatistics and Bioinformatics and rohs@usc.edu raluca.gordan@duke.edu.
authors.forename: Raluca
authors.initials: R
authors.lastname: Gordân
authors.affiliation: Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089; rohs@usc.edu raluca.gordan@duke.edu.
authors.forename: Remo
authors.initials: R
authors.lastname: Rohs
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D004247:DNA / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D007700:Kinetics; D051379:Mice; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D040081:Protein Array Analysis; D011485:Protein Binding; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA
keywords: DNA structure; protein binding microarray; protein−DNA recognition; statistical machine learning; support vector regression