Advanced Heterogeneous Network-Based Graph Neural Network Framework for Predicting Anti-CRISPR Protein Sequences.

Abstract

Anti-CRISPR proteins play a crucial role in bacterial-phage interactions by inhibiting the CRISPR/Cas system and thus enhancing phage survival. Accurately predicting these proteins is essential for understanding phage-host immune interactions and progressing CRISPR/ Cas-based technologies. Current approaches primarily analyze proteins individually, which may overlook the intrinsic similarities and potential connections among protein sequences. This study introduces PACRGNN, a graph neural network framework that creates a heterogeneous protein network by integrating sequence and structural similarities, wherein nodes represent proteins and edges signify their relationships. By combining Graph Attention (GAT) and Graph Sample and Aggregation (GraphSAGE) layers, PACRGNN captures both local and global topological dependencies, while incorporating six protein feature categories to enrich node representations. PACRGNN achieves an accuracy of 0.9577, an F1-Score of 0.9572, and a PRAUC of 0.9876 on the validation set. The model demonstrated superior performance to existing methods on the independent test set derived from NCBI database (Jan.-Oct. 2024).