Year: 2018
Type: article
Abstract: Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Funders: Howard Hughes Medical Institute, Empire State Development's Division of Science, Technology and Innovation, National Institutes of Health, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences, National Center for Research Resources
Awards: Training Program in Computational Biology, G1947595405, G2558552707, Roles and Regulation of wild-type and mutant forms of p53, Core Research Computing Facility, G4062807028, Training in Cellular, Molecular and Biomedical Studies, G6479372462, Predoctoral Training Grant in Genetics and Development, Role of the p53 C-terminal domain in tissue homeostasis, tumor suppression, and oncogenesis, Inferring gene regulatory circuitry from functional genomics data, G9000324803, Interpreting and Deploying Genomic Information During Animal Development
OpenAlex ID: W2755853637