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Related Concept Videos

Protein Folding01:25

Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Protein Folding01:22

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Overview
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Protein Folding Quality Check in the RER01:29

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Conservation of Protein Domains Over Different Proteins02:26

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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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Protein Networks02:26

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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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Related Experiment Video

Updated: Nov 2, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected

Xinyi Qin, Lu Zhang, Min Liu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |June 16, 2021
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    Summary
    This summary is machine-generated.

    This study introduces the ASFold-DNN framework for protein fold recognition, achieving high accuracy on low sequence similarity datasets. The novel approach enhances drug discovery and gene therapy research.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Structural Biology

    Background:

    • Protein fold recognition is crucial for understanding protein function, aiding disease gene therapy and drug development.
    • Existing methods face challenges, particularly with protein datasets exhibiting low sequence similarity.
    • Accurate protein fold recognition is essential for advancing biological and medical research.

    Purpose of the Study:

    • To propose the ASFold-DNN framework for enhanced protein fold recognition.
    • To address the limitations of current methods on low sequence similarity datasets.
    • To improve the accuracy and generalization ability of protein fold recognition models.

    Main Methods:

    • Extraction of four groups of evolutionary features from protein primary structures.
    • Preliminary parameter selection for ACC_HMM and SXG_HMM features.
    • Comparative analysis of feature selection algorithms to determine the optimal scheme.
    • Optimization of Full Connected Neural Network hyperparameters for model construction.

    Main Results:

    • ASFold-DNN achieved high prediction accuracies: 85.28% on DD, 95.00% on EDD, and 88.84% on TG datasets.
    • The framework demonstrated strong generalization ability on ASTRAL186 and LE datasets.
    • Experimental results indicate ASFold-DNN outperforms existing state-of-the-art methods.

    Conclusions:

    • The ASFold-DNN framework provides a prominent solution for protein fold recognition, especially for challenging low sequence similarity cases.
    • The optimized feature selection and neural network architecture contribute to superior performance.
    • ASFold-DNN offers a valuable tool for advancing research in structural biology and related fields.