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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Transcription Factors02:16

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Related Experiment Video

Updated: Apr 16, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Quantitative modeling of transcription factor binding specificities using DNA shape.

Tianyin Zhou1, Ning Shen2, Lin Yang1

  • 1Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics, and Computer Science, University of Southern California, Los Angeles, CA 90089;

Proceedings of the National Academy of Sciences of the United States of America
|March 17, 2015
PubMed
Summary
This summary is machine-generated.

Integrating 3D DNA shape into models significantly improves understanding of transcription factor (TF) binding specificities. This approach reveals TF family-specific structural mechanisms beyond DNA sequence alone.

Keywords:
DNA structureprotein binding microarrayprotein−DNA recognitionstatistical machine learningsupport vector regression

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Area of Science:

  • Genomics
  • Structural Biology
  • Molecular Biology

Background:

  • Transcription factor (TF) binding specificity is crucial for gene regulation but poorly understood.
  • Quantitative models of TF-DNA binding are needed to decipher these mechanisms.
  • Traditional models rely solely on DNA sequence, potentially missing key information.

Purpose of the Study:

  • To develop and evaluate quantitative models of TF binding specificity that incorporate 3D DNA shape information.
  • To compare the performance of shape-augmented models against traditional sequence-based models.
  • To identify novel TF-DNA interaction mechanisms using structural features.

Main Methods:

  • Utilized support vector regression to train models on protein binding microarray (PBM) data for 68 mammalian TFs.
  • Integrated 3D DNA shape features alongside traditional sequence (k-mer) features.
  • Validated models through cross-validation, testing across different PBM designs, and predicting SELEX-seq data.

Main Results:

  • Shape-augmented models demonstrated superior performance compared to sequence-only models.
  • DNA shape features reduced the dimensionality of the modeling feature space.
  • Analysis of model weights revealed TF family-specific structural readout mechanisms missed by sequence analysis.

Conclusions:

  • Incorporating 3D DNA shape into TF binding models enhances predictive accuracy and biological insight.
  • This integrated approach offers a new avenue for understanding TF-DNA interactions and genome regulation.
  • The findings bridge structural biology and genomics, advancing the study of gene function.