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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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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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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

Cooperative Binding of Transcription Regulators

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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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General Transcription Factors01:30

General Transcription Factors

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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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Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

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Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These...
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Co-activators and Co-repressors02:04

Co-activators and Co-repressors

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and

Pengju Ding1, Yifei Wang1, Xinyu Zhang1

  • 1Qingdao University of Science and Technology, China.

Briefings in Bioinformatics
|June 16, 2023
PubMed
Summary

DeepSTF, a novel deep learning model, accurately predicts transcription factor binding sites (TFBSs) by integrating DNA sequence and shape. This approach improves upon existing methods for understanding gene regulation and cellular function.

Keywords:
bidirectional long short-term memoryimproved transformer encoder structuresequence and shapestacked convolutional neural networkstranscription factor binding sites

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate prediction of transcription factor binding sites (TFBSs) is crucial for understanding gene regulation and cellular processes.
  • Existing deep learning models for TFBS prediction face challenges in interpretability and performance.
  • There is a need for improved algorithms that can effectively integrate diverse DNA features.

Purpose of the Study:

  • To introduce DeepSTF, a novel deep learning architecture for enhanced TFBS prediction.
  • To leverage both DNA sequence and shape profiles for more accurate TFBS identification.
  • To explore the utility of transformer encoder structures in TFBS prediction.

Main Methods:

  • Developed DeepSTF, a deep learning model integrating DNA sequence and shape features.
  • Employed stacked convolutional neural networks (CNNs) to extract higher-order DNA sequence features.
  • Utilized an improved transformer encoder structure combined with bidirectional long short-term memory (Bi-LSTM) to extract DNA shape profiles.
  • Integrated sequence and shape features for TFBS prediction.

Main Results:

  • DeepSTF demonstrated superior performance compared to state-of-the-art algorithms on 165 ENCODE ChIP-seq datasets.
  • The study validated the effectiveness of the transformer encoder and the combined sequence-shape strategy.
  • Analysis highlighted the significant contribution of DNA shape features in predicting TFBSs.

Conclusions:

  • DeepSTF offers a powerful and interpretable approach for TFBS prediction.
  • The integration of DNA sequence and shape profiles, enabled by advanced deep learning architectures, significantly enhances prediction accuracy.
  • This work provides valuable insights into the role of DNA shape in transcriptional regulation.