  Accurately modeling the protein fitness landscapes holds great importance for
protein engineering. Recently, due to their capacity and representation
ability, pre-trained protein language models have achieved state-of-the-art
performance in predicting protein fitness without experimental data. However,
their predictions are limited in accuracy as well as interpretability.
Furthermore, such 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. By combining the
techniques of meta-transfer learning, learning to rank, and parameter-efficient
fine-tuning, FSFP can significantly boost the performance of various protein
language models using merely tens of labeled single-site mutants from the
target protein. The experiments across 87 deep mutational scanning datasets
underscore its superiority over both unsupervised and supervised approaches,
revealing its potential in facilitating AI-guided protein design.
