SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction

Abstract

Protein-protein interactions (PPIs) are essential for various biological functions and are crucial in drug discovery, signaling pathways, and network reconstruction. This study presents SPPIPred, an advanced machine learning-based model designed for precise PPI prediction. The SPPIPred model was constructed using five feature extraction methods: Pseudo amino acid composition (PAAC), Composition transition distribution (CTDC), Dipeptide composition (DPC), Word2Vec, and FastText. Among these, FastText emerged as the most effective for encoding protein sequences. Despite the application of feature selection techniques, the analysis revealed that the original raw feature dimensions yielded superior results compared to the selected features. The model used seven machine learning classifiers, including Decision Tree (DT), Extra Trees Classifier (ETC), CatBoost (CAT), XGBoost (XGB), LightGBM (LGBM), Random Forest (RF), and the stacking model named SPPIPred. SPPIPred demonstrated exceptional accuracy rates of 0.9989 in the H pylori dataset and 0.9991 in the S cerevisiae dataset, with Matthews correlation coefficients (MCC) of 0.9982 and 0.9979, respectively. These findings highlight the effectiveness and reliability of the SPPIPred model, offering valuable insights to researchers in the field of bioinformatics and improving applications within bioengineering and pharmaceutical development.