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Related Concept Videos

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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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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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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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Protein-Drug Binding: Determination Methods01:22

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Factors Affecting Protein-Drug Binding: Drug Interactions01:23

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Updated: Mar 26, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Predicting nsSNPs that Disrupt Protein-Protein Interactions Using Docking.

Norman Goodacre, Nathan Edwards, Mark Danielsen

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 27, 2016
    PubMed
    Summary
    This summary is machine-generated.

    A new computational method predicts how genetic variations alter protein interactions. This approach identifies potential therapeutic targets by analyzing protein binding changes, particularly for viral infections like HIV-1.

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    Area of Science:

    • Computational biology
    • Structural biology
    • Genomics

    Background:

    • Individual genome variation results in numerous protein polymorphisms.
    • The impact of these polymorphisms on protein-protein interactions (PPIs) remains largely uncharacterized.

    Purpose of the Study:

    • To develop and validate a computational method for predicting the effect of protein polymorphisms on PPIs.
    • To identify potential therapeutic targets by analyzing disrupted PPIs.

    Main Methods:

    • Integration of the SKEMPI database and CAPRI 4.0 docking benchmark.
    • Utilizing HADDOCK for docking and a random forest classifier to analyze changes in binding affinity.
    • Training and testing the model on mutant protein pairs, including HIV-1 and glioblastoma-related interactions.

    Main Results:

    • The developed model achieved 50% prediction accuracy for non-binders with a 2% false discovery rate on the training set.
    • On independent test sets (HIV-1 and glioblastoma), 50% of non-binders were correctly predicted with a 10% false discovery rate.
    • Identified 10 human-HIV-1 PPIs likely disrupted by rare non-synonymous single-nucleotide polymorphisms (nsSNPs).

    Conclusions:

    • The computational method effectively predicts the impact of protein polymorphisms on PPIs.
    • Identified nsSNPs disrupting human-HIV-1 interactions represent potential novel therapeutic targets for antiviral therapy.