ArticleProceedings of the National Academy of Sciences of the United States of America2018
Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 92 citations in OpenAlex.
- Quantifying protein unfolding kinetics with a high-throughput microfluidic platform.Cell systems · 2026Article
- DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Nucleic acids research · 2026Article
- The adaptive landscapes of three globaleLife · 2026Article
- Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization.PNAS nexus · 2026Article
- Coupling high-throughput protease enzymology with viral replication reveals biochemical constraints of viral fitness.bioRxiv : the preprint server for biology · 2026Article
- Global Evaluation of Congenital Heart Disease-Associated Non-Coding Variants.Research square · 2026Article
- Article
- Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Progress in Aptamer Research and Future Applications.ChemistryOpen · 2025Review
- Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape.Science (New York, N.Y.) · 2025Article
- Mutations to transcription factor MAX allosterically increase DNA selectivity by altering folding and binding pathways.Nature communications · 2025Article
- The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR.Nature communications · 2024Article
- Identification of positions in human aldolase a that are neutral for apparent KArchives of biochemistry and biophysics · 2024Article
- Simplifications and approximations in a single-gene circuit modeling.Scientific reports · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- Dissection of integrated readout reveals the structural thermodynamics of DNA selection by transcription factors.Structure (London, England : 1993) · 2024Article
- Deep flanking sequence engineering for efficient promoter design using DeepSEED.Nature communications · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.