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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...

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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

Shuangquan Zhang1,2, Anjun Ma3, Xuping Xie2

  • 1School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

BMC Genomics
|March 19, 2025
PubMed
Summary

A new model, CacPred, enhances transcription factor-DNA binding prediction accuracy. This deep learning approach outperforms existing methods on ChIP-seq and ChIP-nexus datasets by effectively learning sequence features.

Keywords:
ChIP-seqDeep learningTF-DNA binding predictionTranscription factor

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Transcription factors (TFs) regulate gene expression through DNA binding.
  • Identifying TF binding sites (TFBSs) and cis-regulatory motifs is crucial for understanding gene regulation.
  • Existing deep learning (DL) models, including convolutional neural networks (CNNs), show promise but require accuracy improvements for TF-DNA binding prediction.

Purpose of the Study:

  • To develop and evaluate a novel cascaded convolutional neural network (CNN) model named CacPred for enhanced TF-DNA binding prediction.
  • To explore the role of convolutional operations in TF-DNA binding prediction and compare CacPred against existing DL methods.
  • To assess CacPred's performance on diverse datasets, including Chromatin immunoprecipitation-sequencing (ChIP-seq) and ChIP-nexus.

Main Methods:

  • Development of CacPred, a cascaded CNN model specifically designed for TF-DNA binding prediction.
  • Validation of CacPred using 790 ChIP-seq and seven ChIP-nexus datasets.
  • Comparative analysis of CacPred against six established DL models using nine standard evaluation metrics.

Main Results:

  • CacPred significantly outperformed all comparison models across nine evaluation metrics on both ChIP-seq and ChIP-nexus datasets.
  • Average improvements in accuracy (ACC), Matthews correlation coefficient (MCC), and Area of Eight Metrics Radar (AEMR) were observed: 3.3%, 9.2%, and 6.4% on ChIP-seq data, and 5.5%, 16.8%, and 12.9% on ChIP-nexus data, respectively.
  • Analysis of learned features revealed that CacPred effectively identifies significant motifs, suggesting its capability to capture essential sequence information.

Conclusions:

  • CacPred demonstrates superior performance in TF-DNA binding prediction compared to existing models, particularly on ChIP-seq data.
  • The model's reliance solely on convolutional algorithms, without pooling, suggests that pooling may lead to the loss of critical sequence information.
  • CacPred is presented as an effective, feasible, and publicly available tool for TF-DNA binding prediction.