A Computational Efficiency Feature Extraction Approach for Sequence-based Protein-Protein Interaction Prediction

Protein-protein interactions (PPIs) are essential to understanding biological processes and cellular functions. Experimental methods for identifying PPIs are often timeconsuming and costly, motivating the development of accurate computational prediction approaches. In this paper, we combine embeddings from the state-of-the-art protein language model ESM-2 with handcrafted descriptors including short-linear motifs representation based on the ELM database. The integration of both deep learning-based and biologically meaningful handcrafted features aims to capture global, local characteristics of protein sequences. These extracted features are subsequently fused and used to train machine learning classifiers to determine interaction likelihoods between protein pairs. Experimental results on benchmark PPI datasets demonstrate that our hybrid feature strategy significantly improves prediction performance, highlighting the complementary strengths of learned embeddings and handcrafted descriptors. This work provides a robust and extensible framework for sequence-based PPI prediction, paving the way for scalable and accurate interactome mapping.