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Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli.
Lally, Patrick; Gómez-Romero, Laura; Tierrafría, Víctor H; Aquino, Patricia; Rioualen, Claire; Zhang, Xiaoman; Kim, Sunyoung; Baniulyte, Gabriele; Plitnick, Jonathan; Smith, Carol; Babu, Mohan; Collado-Vides, Julio; Wade, Joseph T; Galagan, James E.
Afiliação
  • Lally P; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215.
  • Gómez-Romero L; Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, Ciudad de México 14610, México.
  • Tierrafría VH; Escuela de Medicina y Ciencias de la Salud, Tecnológico de Monterrey, Ciudad de México, México.
  • Aquino P; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215.
  • Rioualen C; Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, Cuernavaca 62210, Morelos, México.
  • Zhang X; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215.
  • Kim S; Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Avenida Universidad s/n, Cuernavaca 62210, Morelos, México.
  • Baniulyte G; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215.
  • Plitnick J; Department of Biochemistry, University of Regina, Regina, Saskatchewan, SK S4S 0A2, Canada.
  • Smith C; Wadsworth Center, New York State Department of Health, Albany, NY, USA.
  • Babu M; Wadsworth Center, New York State Department of Health, Albany, NY, USA.
  • Collado-Vides J; Wadsworth Center, New York State Department of Health, Albany, NY, USA.
  • Wade JT; Department of Biochemistry, University of Regina, Regina, Saskatchewan, SK S4S 0A2, Canada.
  • Galagan JE; Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA 02215.
bioRxiv ; 2024 May 24.
Article em En | MEDLINE | ID: mdl-38826350
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 used 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 used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article