PLM-interact: extending protein language models to predict protein-protein interactions

Dan Liu, Francesca Young, Kieran D. Lamb, Adalberto Claudio Quiros, Alexandrina Pancheva, Crispin J. Miller, Craig Macdonald (), David L. Robertson () and Ke Yuan ()
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Dan Liu: MRC-University of Glasgow Centre for Virus Research
Francesca Young: MRC-University of Glasgow Centre for Virus Research
Kieran D. Lamb: MRC-University of Glasgow Centre for Virus Research
Adalberto Claudio Quiros: University of Glasgow
Alexandrina Pancheva: Cancer Research UK Scotland Institute
Crispin J. Miller: University of Glasgow
Craig Macdonald: University of Glasgow
David L. Robertson: MRC-University of Glasgow Centre for Virus Research
Ke Yuan: University of Glasgow

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Computational prediction of protein structure from amino acid sequence alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions remains a challenge. Here, we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for protein-protein interaction prediction. Existing models that exploit PLMs use a pre-trained PLM feature set, ignoring that the proteins are physically interacting. We propose PLM-interact, which goes beyond single proteins by jointly encoding protein pairs to learn their relationships, analogous to the next-sentence prediction task from natural language processing. This approach achieves state-of-the-art performance in a widely adopted cross-species protein-protein interaction prediction benchmark: trained on human data and tested on mouse, fly, worm, E. coli and yeast. In addition, we develop a fine-tuning method for PLM-interact to detect mutation effects on interactions. Finally, we report that the model outperforms existing approaches in predicting virus-host interaction at the protein level. Our work demonstrates that large language models can be extended to learn the intricate relationships among biomolecules from their sequences alone.

Date: 2025
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DOI: 10.1038/s41467-025-64512-w

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