  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.
