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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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Transcription Factors02:16

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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 Sequences02:02

Cis-regulatory Sequences

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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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Cooperative Binding of Transcription Regulators02:13

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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.
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Related Experiment Video

Updated: Jun 24, 2025

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Transcription Factor Binding Site Prediction Using CnNet Approach.

M Mohamed Divan Masood, D Manjula, Vijayan Sugumaran

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |June 7, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces CnNet, a deep learning method for predicting transcription factor binding sites by analyzing DNA sequence specificities. CnNet enhances the discovery of gene regulatory elements, improving disease research.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Gene expression control is crucial for understanding biological processes and diseases.
    • Identifying factors that regulate gene expression, such as Transcription Factors (TFs), is vital but challenging.
    • Current methods for discovering TF binding sites and regulatory elements require novel computational approaches.

    Purpose of the Study:

    • To develop and evaluate a deep learning-based computational approach for predicting transcription factor binding.
    • To identify sequence specificities of DNA gene sequences for improved TF binding site prediction.
    • To enhance the accuracy of predicting TF binding scores compared to existing methods.

    Main Methods:

    • Utilized deep learning techniques, specifically Convolution Neural Networks (CNNs).
    • Employed the Multiple Expression Motifs for Motif Elicitation (MEME) technique to discover sequence motifs.
    • Developed a novel approach named CnNet, combining MEME and CNN for TF binding site prediction.

    Main Results:

    • The CnNet approach successfully identifies sequence specificities from experimental data.
    • MEME technique was used to discover motifs indicative of TF binding sites.
    • CNN component of CnNet computes a likelihood score for TF binding with high accuracy.

    Conclusions:

    • The proposed CnNet method offers a scalable, flexible, and unified computational strategy for predicting TF binding.
    • CnNet demonstrates significantly improved accuracy in predicting TF binding scores over existing approaches.
    • This research advances the understanding of gene regulatory mechanisms and disease-related factors.