Large context, deeper insights: Harnessing large language models for advancing protein–protein interaction analysis Journal Article


Authors: U, K.; Zhang, S. M.; Pokharel, S.; Pratyush, P.; Qaderi, F.; Liu, D.; Zhao, J.; Kc, D. B.; Chen, S.
Article Title: Large context, deeper insights: Harnessing large language models for advancing protein–protein interaction analysis
Abstract: Protein–protein interactions (PPIs) are involved in nearly all biological processes. Understanding and analysis of PPI is key to revealing biological networks and identifying new therapeutic targets. Various computational approaches have been proposed as an alternative to the experimental investigation of PPIs. More recently, with the advent of Large Language Models (LLMs), a plethora of approaches using LLMs have been developed, enabling efficient analysis of interaction networks and binding sites directly from protein sequences. These models capture intricate biological patterns, offering scalability and adaptability across diverse datasets. However, challenges remain, including computational costs, data imbalance, and the integration of multimodal information. Advancements in addressing these limitations are set to further enhance the potential of LLMs in protein–protein interaction analysis, driving deeper insights and broader applications in biological research. © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Keywords: nonhuman; proteins; protein analysis; metabolism; computational biology; protein protein interaction; protein; protein binding; databases, protein; prediction; chemistry; amino acid sequence; binding site; binding sites; bioinformatics; protein database; protein interaction mapping; software; commercial phenomena; procedures; protein interaction maps; humans; human; large language model; large language models; large language models (llms); ppi prediction; protein language model; protein–protein interaction (ppi); sequence-based models; chapter
Journal Title: Methods in Molecular Biology
Volume: 2941
ISSN: 1064-3745
Publisher: Humana Press Inc  
Date Published: 2025-01-01
Start Page: 243
End Page: 267
Language: English
DOI: 10.1007/978-1-0716-4623-6_15
PROVIDER: scopus
DOI/URL:
Notes: This was published in the monographic series Large Language Models (LLMs)in Protein Bioinformatics (978-1-0716-4622-9) -- Source: Scopus
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  1. Kai Cheng U
    3 U