Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli

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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Publication Details

Journal
Nature Communications
Published
2025-05-07
DOI
https://doi.org/10.1038/s41467-025-58862-8
Citations
4
Primary Topic
Diffusion and Search Dynamics
Type
article
Field-Weighted Citation Impact
3.22

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article

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli

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Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli

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article en
4 citations

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.

Nature CommunicationsVol. 16(1)
Boston University (US), New York State Department of Health (US), Wadsworth Center (US), University of Regina (CA), University at Albany, State University of New York (US), National Institute of Genomic Medicine (MX), Centre for Genomic Regulation (ES), Instituto de Medicina Genómica (ES), Universidad Autónoma del Estado de Morelos (MX), Universidad Nacional Autónoma de México (MX), Tecnológico de Monterrey (MX)
Universidad Nacional Autónoma de México, Consejo Nacional de Ciencia y Tecnología, Guatemala, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada
Zero hunger
Openalex Percentile: Top 8%
Diffusion and Search Dynamics
3.22
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