ArticleNature communications2025
Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
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- SamT, a novel peptide modulator of a two-component system revealed by the specific activation of a small RNA in Enterobacteriaceae.Nucleic acids research · 2026Article
- The McbR transcription factor links the intracellular folate pool to virulence in enterohemorrhagicmBio · 2026Article
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- The evolution of a NabioRxiv : the preprint server for biology · 2026Article
- Model-guided design of regulatable promoters for synthetic biology.Current opinion in microbiology · 2026Review
- Footprint-seq: a simple method to quantitatively mapbioRxiv : the preprint server for biology · 2026Article
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.bioRxiv : the preprint server for biology · 2025Article
- The Environment-Dependent Regulatory Landscape of thebioRxiv : the preprint server for biology · 2025Article
- Article
- A transcription factor from the cryptic Escherichia coli Rac prophage controls both phage and host operons.Nucleic acids research · 2025Article
- A Cryptic Prophage Transcription Factor Drives Phenotypic Changes via Host Gene Regulation.bioRxiv : the preprint server for biology · 2024Article
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14 authors.
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Abstract
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We use these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We use BoltzNet to quantitatively design novel binding sites, which we validate with biophysical experiments on purified protein. We generate models for 124 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.
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