3Dloop-FPSSM: Predicting Protein–Protein Interactions by Fusing 3D Local Optimal Oriented Pattern and Folded Position-Specific Scoring Matrix

Protein–protein interactions (PPIs) are fundamental to cellular processes, and understanding their mechanisms aids in disease diagnosis, drug target identification, and therapeutic development. Traditional experimental methods for PPI detection are costly and time-consuming, highlighting the need for efficient computational tools. In this study, we introduce a novel sequence-based framework for PPI prediction, which combines position-specific scoring matrices (PSSMs), 3D local optimal orientation patterns (3Dloop), and histogram gradient boosting (HistGB). Protein sequences are first transformed into PSSMs to capture evolutionary conservation, which are then processed into folded PSSMs (FPSSMs) to reveal hidden relationships among discontinuous amino acids. High-dimensional features are extracted using 3Dloop and classified with HistGB. We demonstrate the superiority of this method over random forest (RF) and support vector machine (SVM) models, achieving accuracies of 95.61% on the yeast dataset and 89.93% on the Helicobacter pylori dataset. Ablation studies confirm the effectiveness of each component in the framework. The results show that our approach provides a reliable and efficient solution for PPI prediction.