It works as we last tried sequences from an unreleased glycoprotein-host receptor complex, and it predicted a positive interaction score!

#ProteinLanguageModel#ProteinInteractions#PPIs #MutationEffects#ViruHostInteractions#LLMs #AI #AIforProtein#AIinBiology #AIforScience#FoundationModels#MachineLeanring #Bioinformatics

Thanks to the fantastic AI-in-bio community at the @cvrinfo.bsky.social, @uofgcancersciences.bsky.social
@uofgterrierteam.bsky.social

A huge thanks to Craig Macdonald, @davidlrobertson.bsky.social and Ke Yuan for supervising this work, and other co-authors — Fran Young, @kieranlamb.bsky.social, @adalbertocq.bsky.social, Alexandrina Pancheva, and Crispin Miller.

Code: github.com/liudan111/PL...
Huggingface: huggingface.co/danliu1226

To summarise, PLM-interact extends single-protein PLMs to jointly encode interacting partners. It achieves state-of-the-art performance in cross-species and virus–host PPI prediction tasks and can be fine-tuned to predict mutation effects in human PPIs.

PLM-interact was applied to virus–human PPI prediction. The model outperforms existing approaches, achieving 5.7%, 10.9%, and 11.9% gains in AUPR, F1, and MCC, respectively — effectively capturing virus–host interactions at the protein level.

We further demonstrate examples where PLM-interact correctly predicts the effects of mutations on PPIs associated with human diseases. These results highlight its potential to identify whether disease-associated mutations weaken or strengthen protein interactions.

We fine-tuned PLM-interact to predict the effects of mutations on protein interactions — identifying whether mutations increase or decrease interaction strength. The fine-tuned model significantly outperforms zero-shot PPI models in the mutation-effect prediction task.

PLM-interact achieves state-of-the-art performance on a widely adopted cross-species PPI prediction benchmark — trained on human data and tested on mouse, fly, worm, yeast, and E. coli.

Existing PPI models use pre-trained PLMs to embed each protein separately, ignoring amino acid interactions between proteins. PLM-interact goes beyond single-protein encoding by jointly representing protein pairs to learn their relationships.

Protein language models trained on massive protein sequence datasets capture evolutionary, sequence and structural features — becoming the method of choice for representing proteins in state-of-the-art PPI predictors.

This work is a part of my viroinf PhD research, carried out under the supervision of Craig Macdonald, @davidlrobertson.bsky.social, and Ke Yuan, with HPC support from DiRAC (www.dirac.ac.uk).

PLM-interact: extending protein language models to predict protein-protein interactions - Nature Communications Protein structure can be predicted from amino acid sequences with unprecedented accuracy, yet the prediction of protein–protein interactions remains a challenge. Here, authors present a sequence-based...

Our PLM-interact is out in Nature Communications! We show that jointly encoding protein pairs using protein language models improves protein–protein interaction prediction performance and enables fine-tuning to predict mutation effects in human PPIs. www.nature.com/articles/s41...

GitHub - liudan111/PLM-interact: PLM-interact: extending protein language models to predict protein-protein interactions. PLM-interact: extending protein language models to predict protein-protein interactions. - liudan111/PLM-interact

Code: github.com/liudan111/PL...
Huggingface: huggingface.co/danliu1226

To summarise, PLM-interact extends single-protein PLMs to jointly encode interacting partners. It achieves state-of-the-art performance in cross-species and virus–host PPI prediction tasks and can be fine-tuned to predict mutation effects in human PPIs.

PLM-interact was applied to virus–human PPI prediction. The model outperforms existing approaches, achieving 5.7%, 10.9%, and 11.9% gains in AUPR, F1, and MCC, respectively — effectively capturing virus–host interactions at the protein level.

We further demonstrate examples where PLM-interact correctly predicts the effects of mutations on PPIs associated with human diseases. These results highlight its potential to identify whether disease-associated mutations weaken or strengthen protein interactions.

We fine-tuned PLM-interact to predict the effects of mutations on protein interactions — identifying whether mutations increase or decrease interaction strength. The fine-tuned model significantly outperforms zero-shot PPI models in the mutation-effect prediction task.

PLM-interact achieves state-of-the-art performance on a widely adopted cross-species PPI prediction benchmark — trained on human data and tested on mouse, fly, worm, yeast, and E. coli.

Existing PPI models use pre-trained PLMs to embed each protein separately, ignoring amino acid interactions between proteins. PLM-interact goes beyond single-protein encoding by jointly representing protein pairs to learn their relationships.

Protein language models trained on massive protein sequence datasets capture evolutionary, sequence and structural features — becoming the method of choice for representing proteins in state-of-the-art PPI predictors.

This work is a part of my viroinf PhD research, carried out under the supervision of Craig Macdonald, @davidlrobertson.bsky.social, and Ke Yuan, with HPC support from DiRAC (www.dirac.ac.uk).

We further demonstrate examples where PLM-interact correctly predicts the effects of mutations on PPIs associated with human diseases. These results highlight its potential to identify whether disease-associated mutations weaken or strengthen protein interactions.