Predicting interactions between proteins is one of the most important yet
challenging problems in structural bioinformatics. Intrinsically, potential
function sites in protein surfaces are determined by both geometric and
chemical features. However, existing works only consider handcrafted or
individually learned chemical features from the atom type and extract geometric
features independently. Here, we identify two key properties of effective
protein surface learning: 1) relationship among atoms: atoms are linked with
each other by covalent bonds to form biomolecules instead of appearing alone,
leading to the significance of modeling the relationship among atoms in
chemical feature learning. 2) hierarchical feature interaction: the neighboring
residue effect validates the significance of hierarchical feature interaction
among atoms and between surface points and atoms (or residues). In this paper,
we present a principled framework based on deep learning techniques, namely
Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for
protein surface analysis by bridging chemical and geometric features with
hierarchical interactions. Extensive experiments demonstrate that our method
outperforms the prior state-of-the-art method by 2.3% in site prediction task
and 3.2% in interaction matching task, respectively. Our code is available at
this https URL

在结构生物信息学中，蛋白质间相互作用的预测是最重要且具有挑战性的问题之一。本文提出了一种基于深度学习技术的分层化化学和几何特征交互网络（HCGNet），通过建立化学特征和几何特征之间的分层交互来进行蛋白质表面分析，并通过大量实验证明我们的方法在位点预测任务和相互作用匹配任务上优于之前的最先进方法，分别提高了 2.3% 和 3.2%。