Yujian Lee: Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China. ORCID
Peng Gao: Department of Computer Science, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China. ORCID
Yongqi Xu: Department of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510520, China. ORCID
Ziyang Wang: Department of Science of Chinese Materia Medica, Guangdong Medical University, Dongguan 524023, China. ORCID
Shuaicheng Li: Department of Computer Science, City University of Hong Kong, Hong Kong, China. ORCID
Jiaxing Chen: Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China. ORCID
MOTIVATION: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs. Furthermore, existing models have limitations in effectively predicting the function of newly sequenced proteins that are not included in protein interaction networks. This highlights the need for novel approaches integrating protein structure and sequence data. RESULTS: We introduce Multi-scalE Graph Adaptive neural network (MEGA-GO), highlighting the capability of capturing diverse protein sequence length features from multiple scales. The unique graph adaptive neural network architecture of MEGA-GO enables a more nuanced extraction of graph structure features, effectively capturing intricate relationships within biological data. Experimental results demonstrate that MEGA-GO outperforms mainstream protein function prediction models in the accuracy of Gene Ontology term classification, yielding 33.4%, 68.9%, and 44.6% of area under the precision-recall curve on biological process, molecular function, and cellular component domains, respectively. The rest of the experimental results reveal that our model consistently surpasses the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The source code and data of MEGA-GO are available at https://github.com/Cheliosoops/MEGA-GO.
