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Related Concept Videos

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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Cooperative Binding of Transcription Regulators02:13

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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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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Cis-regulatory Sequences02:02

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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: Nov 27, 2025

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Integrating genome sequence and structural data for statistical learning to predict transcription factor binding

Pengpeng Long1, Lu Zhang1, Bin Huang1

  • 1School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.

Nucleic Acids Research
|December 2, 2020
PubMed
Summary
This summary is machine-generated.

We developed a new method to predict DNA binding sites for tetracycline repressor family transcription regulators. Our approach accurately identifies binding sites, improving upon existing genome sequence-based methods and enabling new discoveries.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Tetracycline repressor family transcription regulators (TFRs) control gene expression.
  • Accurate prediction of TFR binding sites (TFBSs) is crucial for understanding gene regulation.

Purpose of the Study:

  • To develop and validate a computational approach for predicting TFBSs of TFRs.
  • To improve the accuracy and coverage of TFBS prediction compared to existing methods.

Main Methods:

  • Streamlined a genome sequence-based method with quantitative P-values for reliable prediction.
  • Developed a framework incorporating structural data to train a statistical energy function for TFR-TFBS pairing.
  • Benchmarked predictions against experimental data.

Main Results:

  • Successfully predicted TFBSs for 29 out of 30 TFRs using either method.
  • Achieved 59.6% TFR coverage with reliable predictions, significantly higher than the 28.7% from sequence-based methods alone.
  • Identified numerous novel TFBSs not found in public databases.

Conclusions:

  • The developed computational approach accurately predicts TFBSs for TFRs.
  • The statistical energy function reliably models TFBSs and can be used to discover new binding sites.
  • The approach is extendable to other transcription factor families with available structural data.