  Protein classification tasks are essential in drug discovery. Real-world
protein structures are dynamic, which will determine the properties of
proteins. However, the existing machine learning methods, like ProNet (Wang et
al., 2022a), only access limited conformational characteristics and protein
side-chain features, leading to impractical protein structure and inaccuracy of
protein classes in their predictions. In this paper, we propose novel semantic
data augmentation methods, Novel Augmentation of New Node Attributes (NaNa),
and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate
backbone chemical and side-chain biophysical information into protein
classification tasks and a co-embedding residual learning framework.
Specifically, we leverage molecular biophysical, secondary structure, chemical
bonds, and ionic features of proteins to facilitate protein classification
tasks. Furthermore, our semantic augmentation methods and the co-embedding
residual learning framework can improve the performance of GIN (Xu et al.,
2019) on EC and Fold datasets (Bairoch, 2000; Andreeva et al., 2007) by 16.41%
and 11.33% respectively. Our code is available at
https://github.com/r08b46009/Code_for_MIGU_NANA/tree/main.
