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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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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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Updated: Jan 9, 2026

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
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iDeep-Cancer: Predicting Cancer-Related circRNA-RBP Binding Sites Using a Hybrid Network Framework.

Hui Yang, Dangguo Shao, Jie Zhou

    IEEE Transactions on Computational Biology and Bioinformatics
    |December 10, 2025
    PubMed
    Summary
    This summary is machine-generated.

    iDeep-cancer predicts circular RNA (circRNA) and RNA-binding protein (RBP) interactions using only circRNA sequences. This novel deep learning model offers improved accuracy and scalability for identifying crucial binding sites in human diseases.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Circular RNAs (circRNAs) and RNA-binding proteins (RBPs) interact throughout the circRNA lifecycle.
    • Identifying circRNA-RBP binding sites is critical for understanding and regulating human diseases.
    • Existing methods for circRNA-RBP binding site identification suffer from limited feature learning and scalability.

    Purpose of the Study:

    • To develop a novel computational model for predicting circRNA-RBP interactions.
    • To enhance the accuracy and efficiency of circRNA-RBP binding site identification.
    • To provide a sequence-based approach for analyzing circRNA-RBP interactions.

    Main Methods:

    • Developed iDeep-cancer, a hybrid deep learning model utilizing circRNA sequences.
    • Employed feature encoding with four extraction techniques to capture sequence chemical properties.
    • Integrated an improved DenseNet for localized feature learning and BiGRU with self-attention for long-range dependencies.

    Main Results:

    • iDeep-cancer demonstrates superior performance compared to existing state-of-the-art methods.
    • Ablation tests and comparisons across 13 datasets validate the model's efficacy.
    • The model effectively learns complex features from circRNA sequences for interaction prediction.

    Conclusions:

    • iDeep-cancer represents a significant advancement in predicting circRNA-RBP interactions.
    • The model's sequence-based approach overcomes limitations of previous methods.
    • This tool has potential applications in human disease research and therapeutic development.