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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.
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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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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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For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...
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Related Experiment Video

Updated: Nov 11, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning

Siguo Wang1, Qinhu Zhang1,2, Zhen Shen3

  • 1The Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, No. 4800 Caoan Road, Shanghai 201804, China.

Molecular Therapy. Nucleic Acids
|March 26, 2021
PubMed
Summary
This summary is machine-generated.

A new hybrid CNN/RNN model, CRPTS, accurately predicts transcription factor binding sites (TFBSs) by integrating DNA sequence and shape features. This method enhances understanding of transcriptional regulation and outperforms existing computational approaches.

Keywords:
DNA sequenceDNA shape featureshybrid convolutional neural networkrecurrent neural networktranscription factor binding sites

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Transcriptional regulation is crucial but challenging in molecular biology.
  • DNA sequence and structural (shape) features influence transcription factor binding site (TFBS) recognition.
  • Existing computational methods struggle to efficiently integrate both DNA sequence and shape data for TFBS prediction.

Purpose of the Study:

  • To develop an efficient computational model for predicting TFBSs.
  • To integrate both DNA sequence and DNA shape features for improved prediction accuracy.
  • To introduce a novel hybrid deep learning architecture for TFBS prediction.

Main Methods:

  • Proposed a hybrid Convolutional Recurrent Neural Network (CNN/RNN) architecture named CRPTS.
  • Utilized high-throughput genomic sequence data and corresponding DNA shape features.
  • Employed a shared CNN/RNN to extract features from large-scale genomic sequences and identify common patterns between sequence and shape data.

Main Results:

  • CRPTS effectively extracts features from large genomic datasets.
  • Identified common patterns shared between DNA sequences and their structural shapes.
  • Demonstrated that CRPTS can capture local DNA structural information without solely relying on explicit DNA shape data.
  • Achieved superior performance compared to state-of-the-art methods on 66 in vitro datasets.

Conclusions:

  • The proposed CRPTS model offers an efficient and accurate approach for TFBS prediction.
  • Integrating DNA sequence and shape features via a hybrid CNN/RNN architecture significantly improves prediction accuracy.
  • CRPTS provides a valuable tool for studying transcriptional regulation and understanding the interplay between DNA sequence and structure.