We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depending on the levels of details that we wish to capture, protein representations can be computed at different levels, \emph{e.g.}, the amino acid, backbone, or all-atom levels. Importantly, there exist hierarchical relations among different levels. In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations. Our ProNet is very flexible and can be used to compute protein representations at different levels of granularity. We show that, given a base 3D graph network that is complete, our ProNet representations are also complete at all levels. To close the loop, we develop a complete and efficient 3D graph network to be used as a base model, making our ProNet complete. We conduct experiments on multiple downstream tasks. Results show that ProNet outperforms recent methods on most datasets. In addition, results indicate that different downstream tasks may require representations at different levels. Our code is available as part of the DIG library (\url{https://github.com/divelab/DIG}).

本文介绍3D图结构的蛋白质表示学习。使用蛋白质结构构建3D图，循序渐进地学习蛋白质的不同层级表示，并提出了一种新的层次图模型ProNet，以便更好的分类和表征蛋白质。实验结果表明，ProNet性能优于现有方法。

通过完整的三维图网络学习分层蛋白质表示