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Related Concept Videos

Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

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...
Protein Folding01:22

Protein Folding

Overview
Protein and Protein Structure02:15

Protein and Protein Structure

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.
A protein's shape is critical to its function. For example, an enzyme can...
Protein and Protein Structures02:15

Protein and Protein Structures

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.
A protein's shape is critical to its function. For example, an enzyme can...

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A Protocol for Computer-Based Protein Structure and Function Prediction
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Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network

Robert M Bolz, Elijah H Day, Zachary C Drake

    Biorxiv : the Preprint Server for Biology
    |July 10, 2026
    PubMed
    Summary

    Surface-Induced Dissociation native Mass Spectrometry (SID-nMS) data improves protein complex structure prediction. SIDFold, a new deep-learning network, leverages this experimental data for enhanced accuracy in predicting multimeric protein structures.

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    Area of Science:

    • Biochemistry and Structural Biology
    • Computational Biology and Bioinformatics
    • Analytical Chemistry

    Background:

    • Surface-Induced Dissociation native Mass Spectrometry (SID-nMS) provides insights into protein complex connectivity and stoichiometry.
    • Existing deep-learning methods for protein structure prediction do not fully utilize experimental nMS data.
    • Accurate prediction of multimeric protein structures is crucial for understanding biological function.

    Purpose of the Study:

    • To develop a novel deep-learning framework, SIDFold, that integrates SID-nMS data for improved multimeric protein structure prediction.
    • To assess the performance of SIDFold against existing methods and experimental data.
    • To make SIDFold publicly available for the scientific community.

    Main Methods:

    • Development of SIDFold, an AlphaFold-based deep-learning network incorporating SID-nMS experimental data.
    • Benchmarking SIDFold on the BETA protein set to evaluate improvements in Root Mean Square Deviation (RMSD).
    • Validation of SIDFold using experimental SID-nMS data from 20 proteins and comparison with a SID-guided Rosetta docking method.

    Main Results:

    • SIDFold demonstrated improved RMSD in 138 out of 227 cases on the BETA set, with 27 achieving near-native accuracy.
    • Evaluation on 20 proteins with experimental SID-nMS data showed improved RMSD in 18 cases, with five reaching high accuracy.
    • SIDFold outperformed a SID-guided Rosetta docking method, showing improvement in 13 out of 16 proteins.

    Conclusions:

    • Integrating SID-nMS data into deep-learning frameworks like SIDFold significantly enhances the accuracy of multimeric protein structure prediction.
    • SIDFold represents a novel approach, being the first AlphaFold-like network to utilize experimental nMS data for complex prediction.
    • The developed method offers a valuable tool for advancing the field of structural biology and protein complex analysis.