bibtext: @inproceedings{
  jin2024prollm,
  title={Pro{LLM}: Protein Chain-of-Thoughts Enhanced {LLM} for Protein-Protein Interaction Prediction},
  author={Mingyu Jin and Haochen Xue and Zhenting Wang and Boming Kang and Ruosong Ye and Kaixiong Zhou and Mengnan Du and Yongfeng Zhang},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=2nTzomzjjb}
  }
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. Our results highlight the potential of LLMs to transform the field of PPI, serving as a robust potential tool for various categories of biological and medical research. The code is available at: https://anonymous.4open.science/r/ProLLM-AB04.
title: ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
authors: Mingyu Jin Haochen Xue Zhenting Wang Boming Kang Ruosong Ye Kaixiong Zhou Mengnan Du Yongfeng Zhang