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Related Concept Videos

Protein Networks02:26

Protein Networks

4.7K
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 Networks02:26

Protein Networks

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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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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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Predicting Protein Function via Semantic Integration of Multiple Networks.

Guoxian Yu, Guangyuan Fu, Jun Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 23, 2016
    PubMed
    Summary
    This summary is machine-generated.

    SimNet integrates multiple biological data sources and Gene Ontology (GO) knowledge to predict protein function. This computational method achieves accurate protein function prediction efficiently across species.

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

    • Bioinformatics
    • Computational Biology
    • Genomics and Proteomics

    Background:

    • Accurate protein function determination is crucial in the post-genomic era.
    • Large-scale genomic and proteomic data necessitate automated protein function prediction models.
    • Integrating multiple data sources often yields superior prediction accuracy compared to single-source methods.

    Purpose of the Study:

    • To develop a computational method for predicting protein function by integrating diverse biological data sources with biological knowledge.
    • To propose SimNet, a novel approach for semantically integrating multiple functional association networks.

    Main Methods:

    • SimNet utilizes Gene Ontology (GO) annotations to compute semantic similarity between proteins.
    • A semantic kernel is introduced based on protein similarity.
    • A composite network is constructed via weighted summation of individual networks, with weights determined by aligning the network with the semantic kernel.
    • A network-based classifier is applied to the composite network for function prediction.

    Main Results:

    • SimNet demonstrates superior or comparable performance to existing methods in protein function prediction.
    • The method shows significant time efficiency in experiments across Yeast, Human, Mouse, and Fly proteomic data.
    • Experimental results validate the effectiveness of semantic integration for improving prediction accuracy.

    Conclusions:

    • SimNet provides an effective and efficient approach for large-scale protein function prediction.
    • The semantic integration of heterogeneous biological networks enhances prediction accuracy.
    • The proposed method offers a valuable tool for biological research by improving our understanding of protein functions.