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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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HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries
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Predicting CTCF cell type active binding sites in human genome.

Lu Chai1, Jie Gao1, Zihan Li1

  • 1School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, People's Republic of China.

Scientific Reports
|December 31, 2024
PubMed
Summary

The CCCTC-binding factor (CTCF) is crucial for genome regulation. Machine learning identified RAD21/SMC3 and chromatin accessibility as key predictors of CTCF binding activity across cell types.

Keywords:
CTCF binding siteChromatin accessibilityConvolutional neural networksRAD21SMC3

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • The CCCTC-binding factor (CTCF) plays a vital role in genome organization and regulation.
  • Understanding the cell type-specific DNA binding of CTCF is essential for deciphering its diverse biological functions.
  • Current knowledge on the determinants of cell type-active CTCF binding sites remains limited.

Purpose of the Study:

  • To investigate the key factors influencing cell type-specific DNA binding affinity of CTCF.
  • To develop a predictive model for CTCF binding activity using machine learning.
  • To enhance the understanding of CTCF's regulatory roles in the human genome.

Main Methods:

  • Collected and curated ChIP-seq data for CTCF from 67 cell lines within the ENCODE project.
  • Developed a unique dataset of cell type-active CTCF binding sites (CBS).
  • Trained convolutional neural networks (CNNs) to identify patterns associated with CTCF binding activity.

Main Results:

  • Transcription factors RAD21/SMC3 and chromatin accessibility were identified as more predictive of CTCF binding than sequence motifs or histone modifications.
  • Integrated models incorporating these factors achieved Area Under the Precision-Recall Curve (AUPRC) values consistently above 0.868.
  • The findings highlight the predictive power of RAD21/SMC3 and chromatin accessibility for CTCF binding dynamics.

Conclusions:

  • Machine learning frameworks can effectively decipher the complex patterns of CTCF transcription factor binding.
  • RAD21/SMC3 and chromatin accessibility are critical determinants of cell type-specific CTCF binding.
  • This study advances the understanding of CTCF's regulatory functions and genome organization mechanisms.