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

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 20; no. 5; pp. 3163 - 3172
• Main Authors: Berber, Ibrahim, Erten, Cesim, Kazan, Hilal
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological activity; Cancer; Cancer drivers; cancer genomics; Disruption; disruptive mutations; effects of cancer somatic mutations; Feature extraction; feature selection; Genes; Impact prediction; machine learning; Measurement; Mutation; Predators; Predictive models; Protein interaction; protein-protein interactions; Proteins; random forest; Stability analysis; Training data
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2023.3262119

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.