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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.
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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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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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A DNABERT based deep learning framework for predicting transcription factor binding sites.

Pratik Dutta1, Nimisha Ghosh2, Daniele Santoni3

  • 1Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India. pratikdutta@soa.ac.in.

Scientific Reports
|February 3, 2026
PubMed
Summary
This summary is machine-generated.

We developed TFBS-Finder, a deep learning model for predicting Transcription Factor Binding Sites (TFBSs). Our model outperforms existing methods, enhancing understanding of gene regulation.

Keywords:
DNA SequencesDNABERTDeep LearningTranscription Factor Binding Sites

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences called Transcription Factor Binding Sites (TFBSs).
  • Accurate TFBS prediction is crucial for deciphering gene regulatory networks and cellular functions.
  • Existing deep learning models for TFBS prediction offer room for improvement.

Purpose of the Study:

  • To develop an advanced deep learning model, TFBS-Finder, for accurate prediction of Transcription Factor Binding Sites.
  • To leverage pre-trained models and novel attention mechanisms for enhanced feature extraction from DNA sequences.

Main Methods:

  • The TFBS-Finder model integrates pre-trained DNABERT for sequence embedding and capturing long-range dependencies.
  • It incorporates Convolutional Neural Network (CNN), Modified Convolutional Block Attention Module (MCBAM), and Multi-Scale Convolutions with Attention (MSCA) for local feature extraction.
  • The model was trained and validated on 165 ENCODE ChIP-seq datasets.

Main Results:

  • TFBS-Finder demonstrated superior performance in TFBS prediction compared to existing methodologies.
  • Ablation studies and cross-cell line validations confirmed the model's effectiveness.
  • Visual analysis provided insights into the interpretability of the prediction results.

Conclusions:

  • The proposed TFBS-Finder model offers a significant advancement in predicting Transcription Factor Binding Sites.
  • Its architecture effectively combines long-range and local feature extraction for improved accuracy.
  • The model's interpretability and superior performance make it a valuable tool for genomic research.