Our PLM-interact is out in @NatureComms! 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. https://t.co/9unrZqULUS

📢 NEW | Introducing PLM-interact: a new AI-powered protein language model to predict protein-protein interactions Read the article: https://t.co/mOPm5IgN6A Find out more in the mini-podcast:

PLM-interact is out! We learned a lot along the way, from ColBERT to next sentence prediction for PPI, from zero short PPI mutation effect prediction to full model fine-tuning, from not knowing FSDP to burning 30k GPU hours in just a few days. Heroic effort from @DanLiu_

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

A huge thanks to @craig_macdonald, @robertson_lab, and @keyuan1 for supervising this work, and to all my co-authors @FranYoung5, @Kieran12Lamb, @Adalberto_Cq, Alexandrina Pancheva, and Crispin Miller.

💻Code: https://t.co/Bj1OaMs38a 🤗Huggingface:

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 part of my @viroinf PhD research, carried out under the supervision of @craig_macdonald, @robertson_lab, and @keyuan1, with HPC support from @DiRAC_HPC.

EvoMIL uses protein language models & deep learning to predict virus-host associations with improved accuracy & highlights critical viral proteins. #VirusHostInteractions #DeepLearning #Bioinformatics 📄 https://t.co/0IpmAXAhWx EVBC👤: @robertson_lab

Prediction of virus-host associations using protein language models and multiple instance learning @PLOSCompBiol 1. EvoMIL introduces an innovative method for predicting virus-host associations by combining protein language models (PLMs) and attention-based multiple instance

Big news: We just released PLM-interact, a tool for predicting protein-protein interactions, showing a 16-28% improvement over previous methods and even predicting mutation effects on interactions. Here’s the story behind this journey. 🧵👇

Can protein language models be adapted to accurately predict protein-protein interactions across different species and mutation scenarios? @UofGlasgow @biorxivpreprint "PLM-interact: extending protein language models to predict protein-protein interactions" • The prediction

PLM-interact: extending protein language models to predict protein-protein interactions 1. PLM-interact introduces a novel approach to predict protein-protein interactions (PPIs) by jointly encoding protein pairs, leveraging a method similar to “next sentence prediction” in NLP.

A huge thanks to @FranYoung5, @Kieran12Lamb, @Adalberto_Cq, Alexandrina Pancheva, Crispin Miller, @craig_macdonald, and my supervisors @keyuan1, @robertson_lab.