Metalic: Meta-Learning In-Context with Protein Language Models
Jacob Beck Shikha Surana Manus McAuliffe Oliver Bent Thomas D Barrett Juan Jose Garau-Luis Paul Duckworth
Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desired fitness prediction task. As a result of limited data, protein language models (PLMs) are typically trained on general protein sequence modeling tasks, and then fine-tuned, or applied zero-shot, to protein fitness prediction. When no task data is available, the models make strong assumptions about the correlation between the protein sequence likelihood and fitness scores. In contrast, we propose meta-learning over a distribution of standard fitness prediction tasks, and demonstrate positive transfer to unseen fitness prediction tasks. Our method, called Metalic (Meta-Learning In-Context), uses in-context learning and fine-tuning, when data is available, to adapt to new tasks. Crucially, fine-tuning enables considerable generalization, even though it is not accounted for during meta-training. Our fine-tuned models achieve strong results with 18 times fewer parameters than state-of-the-art models. Moreover, our method sets a new state-of-the-art in low-data settings on ProteinGym, an established fitness-prediction benchmark. Due to data scarcity, we believe meta-learning will play a pivotal role in advancing protein engineering.
Meta-Learning, In-context Learning, Tuning, Protein, Fitness Prediction, Property Prediction, Protein Language Model, PLM, Large Language Model
We introduce Metalic, an approach that uses foundation models, in-context learning over related tasks, and fine-tuning for protein fitness prediction. Metalic achieves state-of-the-art results on ProteinGym with less computation and fewer parameters.