GNN-ARG: A Graph Neural Network-based Framework for Predicting Antibiotic Resistance Genes

Antibiotic resistance poses a global health issue that requires innovative techniques for predicting antibiotic resistance genes (ARGs). Traditionally, prediction models depended on sequence-based methods that examined protein sequences to detect ARGs. However, these approaches frequently encounter challenges in identifying the connections between proteins and their interactions. To address this problem, we introduce GNN-ARG, a framework that investigates graph neural network (GNN) architectures to predict ARG from protein sequences using protein interaction graphs. Our method builds a weighted, undirected graph where nodes correspond to proteins and links indicate how similar they are. We utilize existing ESM-2 embeddings as features for nodes to capture specific sequence details. For training GNN-ARG, we employed a dataset combining protein sequences categorized as either ARG or non-ARG from nine public ARG databases. The model was trained with the aim of predicting node labels through binary classification tasks. In the GNN-ARG framework, we conducted a thorough assessment of four well-known GNN models: Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), Graph Attention Networks (GAT), and Graph Sage. Our experimental findings show that GIN outperforms the others providing an accuracy of 93.22% with an F1 score of 0.9161, followed by GCN and Graph Sage, which also show performance closely behind GIN. Despite providing insights into node importance, the GAT model achieves both lower accuracy and F1 score. These results highlight how GNN models can improve the prediction of resistance genes and help us gain a deeper understanding of ways to address antibiotic resistance more effectively in the future research field of bioinformatics by exploring graph-based methods extensively.