Learning from 3D protein structures has gained wide interest in protein
modeling and structural bioinformatics. Unfortunately, the number of available
structures is orders of magnitude lower than the training data sizes commonly
used in computer vision and machine learning. Moreover, this number is reduced
even further, when only annotated protein structures can be considered, making
the training of existing models difficult and prone to over-fitting. To address
this challenge, we introduce a new representation learning framework for 3D
protein structures. Our framework uses unsupervised contrastive learning to
learn meaningful representations of protein structures, making use of proteins
from the Protein Data Bank. We show, how these representations can be used to
solve a large variety of tasks, such as protein function prediction, protein
fold classification, structural similarity prediction, and protein-ligand
binding affinity prediction. Moreover, we show how fine-tuned networks,
pre-trained with our algorithm, lead to significantly improved task
performance, achieving new state-of-the-art results in many tasks.

本文介绍了一种利用无监督对比学习来学习有意义的蛋白质结构表示的框架，其利用蛋白质数据银行中的蛋白质，展示了这些表示可以用于解决多种任务，如蛋白质功能预测、蛋白质折叠分类、结构相似性预测和蛋白质配体结合亲和力预测。此外，作者还表明，使用该算法预训练的微调网络在许多任务中取得了新的最优结果。