ProtFormer-Site: Ultra-fast and Accurate Prediction of Protein-Protein Interaction Sites with Protein Language Model

Identifying protein-protein interaction (PPI) sites is crucial for predicting protein function, uncovering disease mechanisms, and designing drugs. Experimental methods for PPI site identification are often costly and time-consuming, necessitating the development of efficient computational approaches. However, existing methods still face significant challenges in balancing high accuracy with computational efficiency. To address these limitations, we propose ProtFormer-Site, a novel PPI site prediction framework that integrates large protein language models (ESM2 and SaProt) with a parameter-efficient fine-tuning strategy (LoRA). We introduce a specialized ProtFormer backbone featuring a recycling mechanism to iteratively refine residue-level interaction features. The framework includes two variants: a sequence-only model and a structure-enhanced model, catering to different data availabilities. ProtFormer-Site demonstrated outstanding performance on three benchmark datasets, achieving Matthews correlation coefficient (MCC) improvements ranging from 22.4% to 61.5% compared to state-of-the-art methods. Furthermore, ProtFormer-Site demonstrates exceptional scalability, maintaining significantly lower log-transformed inference times across varying sequence lengths compared to state-of-the-art methods. Its computational efficiency makes it uniquely suited for large-scale, high-throughput prediction tasks. These results indicate that ProtFormer-Site offers a robust, accurate, and computationally efficient solution for PPI site prediction.