Residue-Residue Interaction Prediction via Stacked Meta-Learning

Protein-protein interactions (PPIs) are the basis of most biological functions determined by residue-residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta's performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.

Automated summary

Protein-protein interactions are crucial for biological functions, and predicting the residue pairs responsible for these interactions is important for understanding diseases and designing drugs. Computational approaches like RRI-Meta, which integrates different classifiers and considers multiple feature types, have shown superior performance compared to current prediction tools. Conducting experiments using the same data from previous literature, RRI-Meta demonstrated its effectiveness and capability for distinguishing between interacting and noninteracting residues.