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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcription Factors02:16

Transcription Factors

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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Related Experiment Video

Updated: Mar 10, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

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Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.

Yingfei Wang1, Jinsen Li1, Tsu-Pei Chiu1

  • 1Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, United States.

NAR Genomics and Bioinformatics
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

Reverse-complement weight-sharing models improve deep learning accuracy for predicting transcription factor-DNA binding specificity, particularly for low-affinity sites. This advance aids understanding of gene regulation and protein design.

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Related Experiment Videos

Last Updated: Mar 10, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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Area of Science:

  • Computational biology
  • Genomics
  • Molecular biology

Background:

  • Transcription factor-DNA binding specificity is crucial for gene regulation.
  • Deep learning models, including CNNs and SA transformers, have advanced TF-DNA binding prediction.
  • Systematic evaluation of DNA sequence orientation handling in models is needed, especially for low-affinity binding.

Purpose of the Study:

  • To compare different strategies for handling DNA sequence orientations in deep learning models for TF-DNA binding specificity.
  • To evaluate interpretation methods for these models.
  • To identify low-affinity binding sites and their mechanisms.

Main Methods:

  • Utilized SELEX-seq data for eight Exd-Hox heterodimers in Drosophila.
  • Compared canonical models with data augmentation and reverse-complement weight-sharing models (CNNs and SA transformers).
  • Evaluated interpretation methods: Gradient*input, DeconvNet, DeepLIFT, and in silico mutagenesis (ISM).

Main Results:

  • Reverse-complement weight-sharing CNN and SA models trained with augmented data outperformed other approaches.
  • In silico mutagenesis (ISM) was less sensitive to hyperparameter settings compared to other interpretation methods.
  • Identified Exd-Ubx binding at low-affinity sites and proposed biophysical mechanisms.

Conclusions:

  • Reverse-complement strategies enhance deep learning models for TF-DNA binding specificity, especially for low-affinity interactions.
  • ISM is a robust interpretation method for these models.
  • Findings contribute to understanding low-affinity TF binding in gene regulation and inform TF-DNA binding specificity-guided protein design.