Protein Representation Learning by Geometric Structure Pretraining
This addresses the challenge of protein property prediction for biologists by leveraging structural data, though it is incremental as it builds on existing pretraining approaches.
The paper tackles the problem of learning effective protein representations for tasks like function and fold prediction by pretraining on 3D geometric structures, achieving performance on par with or better than state-of-the-art sequence-based methods while using significantly less pretraining data.
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.