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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

13.9K
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 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.
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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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 Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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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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Related Experiment Video

Updated: Oct 6, 2025

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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CPInformer for Efficient and Robust Compound-Protein Interaction Prediction.

Yang Hua, Xiaoning Song, Zhenhua Feng

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 19, 2022
    PubMed
    Summary
    This summary is machine-generated.

    CPInformer enhances compound-protein interaction (CPI) prediction by integrating diverse compound features and multi-scale protein representations. This novel deep learning method improves accuracy and visualizes key interaction regions.

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    A Protocol for Computer-Based Protein Structure and Function Prediction
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    Area of Science:

    • Bioinformatics
    • Computational Chemistry
    • Machine Learning

    Background:

    • Deep learning is the standard for Compound-Protein Interaction (CPI) prediction.
    • Existing methods struggle with compound structural limitations and insufficient protein feature extraction.
    • Simple feature combination in current models leads to information redundancy.

    Purpose of the Study:

    • To develop a novel method, CPInformer, for improved CPI prediction.
    • To address limitations in compound feature extraction and protein representation.
    • To enhance the accuracy and interpretability of CPI prediction models.

    Main Methods:

    • Extracting heterogeneous compound features (graph and functional fingerprints).
    • Generating multi-scale protein features using dense connections (local and global).
    • Applying ProbSparse self-attention guided by compound features to refine protein representations and reduce redundancy.

    Main Results:

    • CPInformer demonstrates superior performance on five benchmark datasets.
    • The method effectively reduces prediction errors from similar compound structures.
    • ProbSparse self-attention successfully eliminates information redundancy and enhances accuracy.

    Conclusions:

    • CPInformer offers a significant advancement in CPI prediction accuracy.
    • The method provides valuable visualization of activated regions in CPI.
    • CPInformer outperforms existing state-of-the-art approaches for CPI prediction.