Ontology-based Protein-Protein Interaction Explanation Using Large Language Models

Abstract Protein-protein interactions (PPIs) play a crucial role in various biological processes, and understanding these interactions is essential for advancing biomedical research. Automated extraction and analysis of PPI information from the rapidly growing scientific literature remains an important challenge. We present a novel ontology-based approach to analyze protein-protein interactions using Large Language Models (LLMs). We applied different learning strategies, namely in-context learning and parameter-efficient instruction fine-tuning for the Llama-2 chat models, to identify keywords in the text that indicate an interaction between a pair of proteins. Our results show that parameter-efficient fine-tuning leads to a performance gain even when the domain is new. The smaller fine-tuned models outperformed the zero-shot performance of much larger models. The keywords identified by the Llama-2 models were mapped to the ontology terms in the Interaction Network Ontology (INO). Our study suggests that a pipeline of an LLM and an ontology is an effective strategy for explaining relations between biomedical entities. This work demonstrates the potential of leveraging ontologies and advanced language models to advance automated PPI analysis from the scientific literature. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵$ Co-first authors; The study was supported by the U.S. National Institute of Allergy and Infectious Disease (U24AI171008 to Y.H. and J.H.), and A.O. was partially supported by the GEBIP Award of the Turkish Academy of Sciences. 1 The INO term has the IRI: http://purl.obolibrary.org/obo/INO0000117. 2 The INO ontology is available on: http://purl.obolibrary.org/obo/ino.owl 3 The INO GitHub repository: https://github.com/INO-ontology/ino