Title: One protein is all you need
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: proteins, generalization, self-supervised learning, model customization, test-time training

Abstract: Generalization beyond training data remains a central challenge in machine learning for biology. A common way to enhance generalization is self-supervised pre-training on large datasets. However, aiming to perform well on all possible proteins can limit a model’s capacity to excel on any specific one, whereas practitioners typically need accurate predictions for individual proteins they study, often not covered in training data. To address this limitation, we propose a method that enables self-supervised customization of protein language models to one target protein at a time, on the fly, and without assuming any additional data. We show that our Protein Test-Time Training (ProteinTTT) method consistently enhances generalization across different models, their sizes, and datasets. ProteinTTT improves structure prediction for challenging targets, achieves new state-of-the-art results on protein fitness prediction, and enhances function prediction on two tasks. We also demonstrate ProteinTTT on two challenging case studies. We show that customization via ProteinTTT enables more accurate antibody–antigen loop modeling and improves 17% of structures in the Big Fantastic Virus Database, delivering improved predictions where general-purpose AlphaFold2 and ESMFold struggle.

TL;DR: Per-protein self-supervised customization improves generalization across models and tasks.