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Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions
Abstract:
Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.
Insights
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.
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.
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