ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang, Ruosong Ye, Kaixiong Zhou, Mengnan Du, Yongfeng Zhang
TL;DR
ProLLM reframes protein-protein interaction prediction as a natural language reasoning task by encoding signaling pathways into Protein Chain of Thought (ProCoT) prompts for LLMs. It couples ProCoT with ProtTrans-based embedding replacement and instruction fine-tuning on a protein-knowledge corpus (Mol), enabling multi-step, pathway-aware inferences that capture non-physical, signaling-chain connections. Across four benchmark datasets (Human, SHS27K, SHS148K, STRING), ProLLM outperforms traditional ML, GNN-based, and prior LLM-based approaches in micro-F1 and demonstrates strong generalization. This work suggests a promising direction for integrating structured biological data with large language models to advance protein interaction discovery and functional network analyses, with potential implications for drug discovery and systems biology.
Abstract
The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.