ArticleProceedings of the National Academy of Sciences of the United States of America2015
Quantitative modeling of transcription factor binding specificities using DNA shape.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers.
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Who cites it
144 citing papers in PubMed.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- The adaptive landscapes of three globaleLife · 2026Article
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.NAR genomics and bioinformatics · 2026Article
- Interplay between circadian and other transcription factors-Implications for cycling transcriptome reprogramming.FEBS letters · 2026Review
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- How negative sampling shapes the performance of transcription factor binding site prediction models.Bioinformatics (Oxford, England) · 2026Article
- DNA breathing and shape encode distinct mechanisms of transcription factor binding.Bioinformatics advances · 2026Article
- Novel fold and wing structure of Forkhead transcription factor facilitate DNA binding.Nucleic acids research · 2025Article
- Article
- deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- De novo design of insulated cis-regulatory elements based on deep learning-predicted fitness landscape.Nucleic acids research · 2025Article
- Mechanisms for DNA Interplay in Eukaryotic Transcription Factors.Annual review of biophysics · 2025Review
- PhWRKY30 activates salicylic acid biosynthesis to positively regulate antiviral defense response in petunia.Horticulture research · 2025Article
- Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- Protein target search diffusion-association/dissociation free energy landscape around DNA binding site with flanking sequences.Biophysical journal · 2025Article
- ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions.bioRxiv : the preprint server for biology · 2025Article
- Contribution of DNA breathing to physical interactions with transcription factors.bioRxiv : the preprint server for biology · 2025Article
84 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
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.
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