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<p>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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Lei Wang,
  • Shali Dong,
  • Han Zhang,
  • Yuchen Chen,
  • Xudong Li,
  • Yan Wang,
  • Zhidong Xue

摘要

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.

Graphical Abstract