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

Patrick Lally, Laura Gómez-Romero, Víctor H. Tierrafría, Patricia Aquino, Claire Rioualen, Xiaoman Zhang, Sunyoung Kim, Gabriele Baniulyte, Jonathan Plitnick, Carol Smith, Mohan Babu, Julio Collado-Vides, Joseph T. Wade and James E. Galagan ()
Additional contact information
Patrick Lally: 44 Cummington Mall
Laura Gómez-Romero: Ciudad de México
Víctor H. Tierrafría: 44 Cummington Mall
Patricia Aquino: 44 Cummington Mall
Claire Rioualen: Cuernavaca
Xiaoman Zhang: 44 Cummington Mall
Sunyoung Kim: Regina
Gabriele Baniulyte: New York State Department of Health
Jonathan Plitnick: New York State Department of Health
Carol Smith: New York State Department of Health
Mohan Babu: Regina
Julio Collado-Vides: 44 Cummington Mall
Joseph T. Wade: New York State Department of Health
James E. Galagan: 44 Cummington Mall

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-58862-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58862-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-58862-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().