Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

ABSTRACT: Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC11219809 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature