  Motivation: Protein embedding, which represents proteins as numerical
vectors, is a crucial step in various learning-based protein
annotation/classification problems, including gene ontology prediction,
protein-protein interaction prediction, and protein structure prediction.
However, existing protein embedding methods are often computationally expensive
due to their large number of parameters, which can reach millions or even
billions. The growing availability of large-scale protein datasets and the need
for efficient analysis tools have created a pressing demand for efficient
protein embedding methods.
  Results: We propose a novel protein embedding approach based on multi-teacher
distillation learning, which leverages the knowledge of multiple pre-trained
protein embedding models to learn a compact and informative representation of
proteins. Our method achieves comparable performance to state-of-the-art
methods while significantly reducing computational costs and resource
requirements. Specifically, our approach reduces computational time by ~70\%
and maintains almost the same accuracy as the original large models. This makes
our method well-suited for large-scale protein analysis and enables the
bioinformatics community to perform protein embedding tasks more efficiently.
