  This paper focuses on three critical problems on protein classification.
Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to
understand the properties of enzymes. However, one CAZyme may belong to several
classes. This leads to Multi-label CAZyme classification. Secondly, to capture
information from the secondary structure of protein, protein classification is
modeled as graph classification problem. Thirdly, compound-protein interactions
prediction employs graph learning for compound with sequential embedding for
protein. This can be seen as classification task for compound-protein pairs.
This paper proposes three models for protein classification. Firstly, this
paper proposes a Multi-label CAZyme classification model using CNN-LSTM with
Attention mechanism. Secondly, this paper proposes a variational graph
autoencoder based subspace learning model for protein graph classification.
Thirdly, this paper proposes graph isomorphism networks (GIN) and
Attention-based CNN-LSTM for compound-protein interactions prediction, as well
as comparing GIN with graph convolution networks (GCN) and graph attention
networks (GAT) in this task. The proposed models are effective for protein
classification. Source code and data are available at
https://github.com/zshicode/GNN-AttCL-protein. Besides, this repository
collects and collates the benchmark datasets with respect to above problems,
including CAZyme classification, enzyme protein graph classification,
compound-protein interactions prediction, drug-target affinities prediction and
drug-drug interactions prediction. Hence, the usage for evaluation by benchmark
datasets can be more conveniently.
