Predicting the involvement of polyQ- and polyA in protein-protein interactions by their amino acid context

Homorepeats, specifically polyglutamine (polyQ) and polyalanine (polyA), are often implicated in protein-protein interactions (PPIs). So far, a method to predict the participation of homorepeats in protein interactions is lacking. We propose a machine learning approach to identify PPI-involved polyQ and polyA regions within the human proteome based on known interacting regions. Using the dataset of human homorepeats, we identified 157 polyQ and 745 polyA regions potentially involved in PPIs. Machine learning models, trained on amino acid context and homorepeat length, demonstrated high precision (0.90-0.98) but variable recall (0.42-0.85). Random forest outperformed other models (AUC polyQ = 0.686, AUC polyA = 0.732) using the positions surrounding the homorepeat -10 to +10. Integrating paralog information marginally improved predictions but was excluded for model simplicity. Further optimization revealed that for polyQ, using amino acid surrounding positions from -6 to +6 increased AUC to 0.715. For polyA, no improvement was found. Incorporating coiled coil overlap information enhanced polyA predictions (AUC = 0.745) but not polyQ. Finally, we applied these models to predict PPI involvement across all polyQ and polyA regions, identifying potential interactions. Case studies illustrated the method's predictive capacity, highlighting known interacting regions with high scores and elucidating potential false negatives.