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.

Can protein language models be adapted to accurately predict protein-protein interactions across different species and mutation scenarios? University of Glasgow bioRxiv "PLM-interact: extending protein language models to predict protein-protein interactions" • The prediction

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. 🧵👇

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

EvoMIL uses protein language models & deep learning to predict virus-host associations with improved accuracy & highlights critical viral proteins. #VirusHostInteractions #DeepLearning #Bioinformatics 📄 doi.org/10.1371/journa… EVBC👤: Robertson

📢 NEW | Introducing PLM-interact: a new AI-powered protein language model to predict protein-protein interactions Read the article: lnkd.in/ebM5xRy4 Find out more in the mini-podcast: youtu.be/GISFOMWaajs

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 Dan Liu