  Learning effective protein representations is critical in a variety of tasks
in biology such as predicting protein function or structure. Existing
approaches usually pretrain protein language models on a large number of
unlabeled amino acid sequences and then finetune the models with some labeled
data in downstream tasks. Despite the effectiveness of sequence-based
approaches, the power of pretraining on known protein structures, which are
available in smaller numbers only, has not been explored for protein property
prediction, though protein structures are known to be determinants of protein
function. In this paper, we propose to pretrain protein representations
according to their 3D structures. We first present a simple yet effective
encoder to learn the geometric features of a protein. We pretrain the protein
graph encoder by leveraging multiview contrastive learning and different
self-prediction tasks. Experimental results on both function prediction and
fold classification tasks show that our proposed pretraining methods outperform
or are on par with the state-of-the-art sequence-based methods, while using
much less pretraining data. Our implementation is available at
https://github.com/DeepGraphLearning/GearNet.
