PCPredG: Protein Complex Prediction Using Graphlet Features

Proteins interact with other proteins and bio-molecules to form a complex and execute key biological functions in a living organism, and respond to several environmental signals. Designing efficient predictive models for protein complexes is a challenging task with limited coverage in the contemporary literature. With this motivation, we have developed a novel method, PCPredG, for 3-node protein complex prediction from PPI networks using 5-node graphlet features. CORUM protein complex repository has been used to curate positive and negative data samples with the help of MCODE and MCL clustering algorithms. During experiments, Random Forest(RF) and SVM classifiers are trained with 1000 positive 3-node complexes in 10-fold cross-validation setup and with 1:1 to 1:10 positive-negative proportions. In parallel, we have implemented the state-of-the-art GCN with polarised message-passing, GAT and an ensemble of GCN and GAT in both balanced and imbalanced setups. We also introduced a 10-fold quality consensus on the hold-out set across all the experiments. We have achieved the best performances with the RF classifier in both balanced and imbalanced experiments.