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Related Concept Videos

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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Cooperative Binding of Transcription Regulators02:13

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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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Conserved Binding Sites01:49

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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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General Transcription Factors01:30

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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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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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A structure-based Multiple-Instance Learning approach to predicting in vitro transcription factor-DNA interaction.

Zhen Gao, Jianhua Ruan

    BMC Genomics
    |April 29, 2015
    PubMed
    Summary
    This summary is machine-generated.

    MIL3D, a novel method, predicts transcription factor binding using DNA structural properties and multiple-instance learning. It outperforms existing methods, aiding the study of biological regulatory networks.

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    Area of Science:

    • Molecular Biology
    • Genomics
    • Bioinformatics

    Background:

    • Transcriptional regulation is a key area in molecular biology.
    • In vitro protein-binding microarray experiments enhance understanding of transcription factor-DNA interactions.
    • Accurate prediction of these interactions is crucial for deciphering regulatory networks.

    Purpose of the Study:

    • To introduce MIL3D, a novel computational method for predicting in vitro transcription factor binding.
    • To leverage multiple-instance learning and DNA structural properties for improved prediction accuracy.
    • To provide a tool for advancing the study of biological regulatory networks.

    Main Methods:

    • Developed MIL3D, a multiple-instance learning approach.
    • Incorporated DNA structural properties into the prediction model.
    • Utilized in vitro transcription factor binding data for training and validation.

    Main Results:

    • MIL3D demonstrated superior performance compared to simple-instance learning and k-mer counting methods for 19 out of 20 mouse transcription factors.
    • The method effectively identifies binding sites even when strong sequence consensus is absent.
    • MIL3D successfully utilizes subtle structural similarities in DNA for accurate predictions.

    Conclusions:

    • The integration of multiple-instance learning and DNA structural properties offers a powerful approach for predicting transcription factor binding.
    • MIL3D shows significant potential for advancing research in biological regulatory networks.
    • This method can enhance the understanding of gene regulation mechanisms.