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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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De Novo Protein Structure Prediction by Model Quality Assessment Dynamic Feedback Mechanism Using Deep Learning.

Jun Liu, Guang-Xing He, Kai-Long Zhao

    IEEE Transactions on Computational Biology and Bioinformatics
    |November 7, 2025
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    Summary
    This summary is machine-generated.

    DGMFold improves de novo protein structure prediction by integrating model quality assessment into a closed-loop feedback system. This iterative refinement enhances accuracy, particularly for challenging protein targets where other methods falter.

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    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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    Area of Science:

    • Computational biology
    • Structural bioinformatics
    • Machine learning in protein science

    Background:

    • Accurate de novo protein structure prediction is crucial but challenging, especially without homologous templates or strong evolutionary data.
    • Current end-to-end methods like AlphaFold2 are accurate but lack transparency and flexibility for external evaluation.
    • Integrating model quality assessment (MQA) into prediction pipelines offers a potential avenue for iterative accuracy improvement.

    Purpose of the Study:

    • To investigate the integration of MQA as a closed-loop feedback mechanism for iterative de novo protein structure prediction.
    • To develop and evaluate DGMFold, a novel method employing a feedback loop between geometric constraint prediction, structural simulation, and quality evaluation.
    • To assess DGMFold's performance against state-of-the-art methods, including AlphaFold2 and RoseTTAFold, on benchmark and challenging protein targets.

    Main Methods:

    • DGMFold utilizes a three-component feedback loop: GeomNet for geometric constraint prediction from MSAs, a structural simulation module, and EmaNet for model quality evaluation.
    • GeomNet predicts inter-residue geometric constraints using an improved residual neural network, guiding structure folding.
    • EmaNet estimates structure accuracy (distance deviation, lDDT) and feeds this information back to GeomNet for iterative refinement.

    Main Results:

    • The closed-loop feedback mechanism in DGMFold significantly enhances prediction performance.
    • DGMFold demonstrated superior accuracy compared to trRosetta and RaptorX on benchmark and CASP14 FM targets.
    • DGMFold achieved higher accuracy than AlphaFold2 and RoseTTAFold on specific subsets of human proteins where these methods had lower TM-scores.

    Conclusions:

    • DGMFold's iterative, feedback-driven approach effectively improves de novo protein structure prediction accuracy.
    • The integration of MQA within a closed-loop system represents a promising strategy for advancing protein structure prediction.
    • DGMFold offers a competitive alternative, particularly for challenging protein targets, showcasing the benefits of integrating predictive and evaluative components.