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Related Concept Videos

Protein Networks02:26

Protein Networks

3.9K
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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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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Protein-Protein Interfaces

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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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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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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

Jie Yang, Yapeng Li, Guoyin Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel AI model, the Knowledge Graph Fused Graph Neural Network (KGF-GNN), for accurate protein-protein interaction (PPI) prediction. The KGF-GNN model enhances understanding of cellular mechanisms and aids drug development by integrating diverse biological data.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Artificial Intelligence in Life Sciences

    Background:

    • Protein-protein interactions (PPIs) are fundamental to cellular functions, disease pathology, and drug discovery.
    • Existing artificial intelligence (AI) methods for PPI prediction often suffer from fragmented data handling and suboptimal feature extraction.
    • Limitations in non-end-to-end learning frameworks hinder comprehensive analysis of complex biological networks.

    Purpose of the Study:

    • To develop a novel end-to-end learning model for accurate and comprehensive protein-protein interaction (PPI) prediction.
    • To address the limitations of current AI approaches by integrating diverse biological data through a unified framework.
    • To improve the prediction accuracy of PPIs by optimizing feature extraction and fusion processes.

    Main Methods:

    • Construction of a Protein Associated Network (PAN) integrating proteins, drugs, diseases, RNA, and protein structures.
    • Application of Graph Neural Networks (GNNs) for extracting topological and semantic features from the PAN and PPI networks.
    • Utilization of a multi-layer perceptron for end-to-end feature fusion and PPI prediction.

    Main Results:

    • The proposed Knowledge Graph Fused Graph Neural Network (KGF-GNN) model demonstrates high accuracy in PPI prediction.
    • KGF-GNN significantly outperforms existing state-of-the-art models on real-world PPI datasets.
    • The end-to-end learning framework ensures optimized feature extraction and fusion for enhanced prediction.

    Conclusions:

    • The KGF-GNN model offers a more precise approach to predicting protein-protein interactions.
    • This advancement has profound implications for biological research, disease mechanism understanding, and therapeutic development.
    • The study highlights the potential of integrated AI approaches in bioinformatics for advancing life sciences.