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Related Concept Videos

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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Structural Protein Function01:56

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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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 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.
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction.

Vikash Kumar, Akshay Deepak, Ashish Ranjan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Lite-SeqCNN, a novel framework using dilated convolutional neural networks (CNNs), effectively predicts protein function by capturing both short- and long-range amino acid interactions. This lightweight model offers improved performance with fewer parameters than existing deep learning methods.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Machine Learning in Biology

    Background:

    • Protein function is determined by amino acid interactions.
    • Convolutional Neural Networks (CNNs) excel at sequential data but struggle with long-range dependencies.
    • Dilated CNNs enhance the ability to capture both short- and long-range interactions.

    Purpose of the Study:

    • To develop a simple, lightweight, sequence-only framework for protein function prediction (PFP).
    • To leverage dilated CNNs for efficient capture of diverse interaction ranges within protein sequences.
    • To reduce the number of trainable parameters compared to existing complex PFP models.

    Main Methods:

    • Proposed Lite-SeqCNN framework utilizing sub-sequences and dilated CNNs.
    • Implemented varying dilation rates to capture both short- and long-range interactions.
    • Developed Lite-SeqCNN+ as an ensemble of three Lite-SeqCNN models with different segment sizes.

    Main Results:

    • Lite-SeqCNN demonstrates efficient capture of short- and long-range interactions.
    • The framework uses 50-75% fewer trainable parameters than contemporary deep learning models.
    • Lite-SeqCNN+ ensemble model achieved up to 5% improvement over state-of-the-art methods (Global-ProtEnc Plus, DeepGOPlus, GOLabeler) on UniProt datasets.

    Conclusions:

    • Lite-SeqCNN offers a computationally efficient and effective approach to protein function prediction.
    • The dilated CNN architecture is well-suited for capturing complex interaction patterns in protein sequences.
    • Ensembling Lite-SeqCNN models further enhances prediction accuracy, outperforming existing methods.