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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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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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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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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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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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Related Experiment Video

Updated: Jan 5, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

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Predicting in-vitro Transcription Factor Binding Sites Using DNA Sequence + Shape.

Qinhu Zhang, Zhen Shen, De-Shuang Huang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 22, 2019
    PubMed
    Summary
    This summary is machine-generated.

    We developed DLBSS, a deep learning framework integrating DNA sequence and shape, to predict transcription factor binding sites (TFBSs). This method significantly improves TFBS prediction accuracy, advancing our understanding of protein-DNA interactions.

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    DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Transcription factor binding sites (TFBSs) are crucial for gene regulation.
    • Convolutional neural networks (CNNs) have shown success in predicting TFBSs from DNA sequences.
    • Protein-DNA binding is influenced by both DNA sequence and DNA shape properties.

    Purpose of the Study:

    • To develop a deep learning framework (DLBSS) that integrates DNA sequence and shape features for enhanced TFBS prediction.
    • To improve the understanding of protein-DNA binding preferences by leveraging combined sequence and shape information.
    • To explore the efficacy of deep learning in capturing complex patterns in sequence and shape data.

    Main Methods:

    • Developed a deep-learning-based sequence + shape framework (DLBSS).
    • Utilized a shared CNN to identify common patterns in DNA sequences and their corresponding shape features.
    • Concatenated sequence and shape features for predicting TFBSs.
    • Validated the method on 66 in-vitro datasets from universal protein binding microarrays (uPBMs).

    Main Results:

    • DLBSS significantly improves the performance of predicting TFBSs compared to existing methods.
    • Demonstrated the effectiveness of integrating DNA sequence and shape properties using a shared CNN.
    • Experimental results confirm the superior predictive power of the DLBSS framework.

    Conclusions:

    • The DLBSS framework offers a powerful approach for TFBS prediction by combining DNA sequence and shape information with deep learning.
    • Integrating DNA shape properties alongside sequence data enhances the accuracy of predicting protein-DNA binding preferences.
    • Future work can explore deeper CNN architectures within the DLBSS framework for further performance gains.