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Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions.

Ibrahim Berber, Cesim Erten, Hilal Kazan

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    This study introduces Predator, a new model that accurately predicts how mutations disrupt protein interactions crucial for cancer research. It identifies potential cancer-driving genes by analyzing somatic mutations at protein interfaces.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Cancer Research

    Background:

    • Protein-protein interactions are vital for biological processes and understanding mutations disrupting them is key for cancer research.
    • Existing methods often overlook mutation-specific effects on protein interactions, focusing instead on protein stability.

    Purpose of the Study:

    • To develop a computational model that predicts the impact of somatic mutations on protein-protein interactions.
    • To identify genes and their interaction partners frequently affected by disruptive interface mutations in cancer.

    Main Methods:

    • Developed an ensemble model named Predator to classify interface mutations as disruptive or non-disruptive.
    • Trained and validated Predator using predicted effects of mutations on specific protein-protein interactions.
    • Applied Predator to TCGA cancer cohorts for comprehensive analysis at multiple levels (cohort, patient, gene).

    Main Results:

    • Predator demonstrated superior prediction accuracy compared to existing methods.
    • Identified genes with interface mutations that frequently disrupt protein interactions across various cancer cohorts.
    • Observed patterns of mutual exclusivity between identified genes and their disrupted partners.

    Conclusions:

    • Predator accurately predicts disruptive mutations impacting protein-protein interactions, outperforming current approaches.
    • The model aids in identifying potential cancer drivers by analyzing mutation effects on protein interactions.
    • Findings reveal significant patterns of gene and interaction disruption in cancer, suggesting therapeutic targets.