In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 16 ( 2018-04-17)
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.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1714376115
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2018
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12