  Automatic protein function prediction (AFP) is classified as a large-scale
multi-label classification problem aimed at automating protein enrichment
analysis to eliminate the current reliance on labor-intensive wet-lab methods.
Currently, popular methods primarily combine protein-related information and
Gene Ontology (GO) terms to generate final functional predictions. For example,
protein sequences, structural information, and protein-protein interaction
networks are integrated as prior knowledge to fuse with GO term embeddings and
generate the ultimate prediction results. However, these methods are limited by
the difficulty in obtaining structural information or network topology
information, as well as the accuracy of such data. Therefore, more and more
methods that only use protein sequences for protein function prediction have
been proposed, which is a more reliable and computationally cheaper approach.
However, the existing methods fail to fully extract feature information from
protein sequences or label data because they do not adequately consider the
intrinsic characteristics of the data itself. Therefore, we propose a
sequence-based hierarchical prediction method, DeepGATGO, which processes
protein sequences and GO term labels hierarchically, and utilizes graph
attention networks (GATs) and contrastive learning for protein function
prediction. Specifically, we compute embeddings of the sequence and label data
using pre-trained models to reduce computational costs and improve the
embedding accuracy. Then, we use GATs to dynamically extract the structural
information of non-Euclidean data, and learn general features of the label
dataset with contrastive learning by constructing positive and negative example
samples. Experimental results demonstrate that our proposed model exhibits
better scalability in GO term enrichment analysis on large-scale datasets.
