P 3: A Framework for Predicting Protein-Protein Interactions Using Large Language Models

The prediction of protein-protein interactions (PPIs) is essential for understanding biological functions and disease mechanisms. Traditional methods for PPI prediction often focus on physical interactions, overlooking the complex indirect relationships mediated by intermediate proteins. The recent advances in Large Language Models (LLMs) present a novel opportunity to address these challenges. By treating protein sequences as natural language, LLMs can capture both direct and indirect interactions, enhancing prediction capabilities. In this paper, we propose a new framework that leverages a BERT-based LLM fine-tuned specifically for PPI prediction. Our model encodes protein sequences into high-dimensional embeddings, capturing long-range dependencies between amino acids, which are critical for identifying PPIs. By fine-tuning the LLM on a novel PPI dataset, we achieve an accuracy of 93% and a F1-score of 83%. The proposed framework shows improved performance in terms of both prediction accuracy and generalization, demonstrating the potential of LLMs to revolutionize PPI prediction and provide valuable insights for fields such as drug discovery and molecular biology.