GrapePPI: A Deep Learning Framework for Grape Protein–Protein Interaction Prediction Using ESM Embeddings

Protein–protein interactions (PPIs) are fundamental to biological processes, yet experimental identification of PPIs remains time-consuming and costly, particularly for crop species with limited data. Grape (Vitis vinifera) is a globally important fruit crop that would benefit from improved computational tools for PPI prediction to support functional genomics and molecular breeding. Here, we present GrapePPI, a deep learning framework specifically designed for grape PPI prediction that leverages pre-trained ESM (Evolutionary Scale Modeling) protein embeddings. GrapePPI employs a four-component architecture: ESM embedding extraction, sequence encoding, feature combination, and multi-layer interaction prediction. We evaluated GrapePPI on grape-specific datasets with balanced and imbalanced class distributions, as well as benchmark datasets from yeast and Arabidopsis. On grape data, GrapePPI significantly outperformed state-of-the-art methods including DeepFE-PPI, PIPR, and ESMAraPPI, achieving F1 scores of 89.34% and 85.43% on balanced and imbalanced datasets, respectively, with PR AUC values of 95.29% and 90.87%. GrapePPI also demonstrated strong cross-species generalization, outperforming competing methods on yeast datasets and achieving performance comparable to specialized plant models on Arabidopsis data. Our results establish GrapePPI as an effective and robust tool for grape PPI prediction, with practical applications in functional genomics research and crop improvement programs.