期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2015
卷号：112
期号：15
页码：4654-4659
DOI：10.1073/pnas.1422023112
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceGenomes 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.
关键词：protein−DNA recognition ; statistical machine learning ; support vector regression ; protein binding microarray ; DNA structure
