[2411.02109] One protein is all you need

Computer Science > Machine Learning arXiv:2411.02109 (cs) [Submitted on 4 Nov 2024 (v1), last revised 1 Mar 2026 (this version, v3)] Title:One protein is all you need Authors:Anton Bushuiev, Roman Bushuiev, Olga Pimenova, Nikola Zadorozhny, Raman Samusevich, Elisabet Manaskova, Rachel Seongeun Kim, Hannes Stärk, Jiri Sedlar, Martin Steinegger, Tomáš Pluskal, Josef Sivic View a PDF of the paper titled One protein is all you need, by Anton Bushuiev and 11 other authors View PDF HTML (experimental) 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 experimentalists 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. Through two...