RIT-HetGE: A residue interaction type-aware heterogeneous graph-embedding model for predicting protein thermal stability.

Abstract

Accurately predicting protein thermal stability is crucial for understanding protein function, guiding protein engineering, and advancing biomedical and industrial applications. Although research on protein thermal stability has advanced with structure-based representation learning, most existing methods embed protein structures as homogeneous graphs, which are unable to capture the intricate and heterogeneous characteristics of residue-residue interactions. To address this limitation, we propose a Residue Interaction Type-Aware Heterogeneous Graph Embedding model (RIT-HetGE) that utilizes intra-interaction-type-aware convolutions for local structure learning and employs an inter-interaction-type-aware attention mechanism to fuse interaction-specific features. Theoretical analysis based on Rademacher complexity provides generalization guarantees. Experiments on a large-scale protein structure dataset demonstrate that our model outperforms baseline models and aggregates diverse interaction types to enhance protein representation. RIT-HetGE also exhibits strong interpretability; the intra-interaction-type-aware layer enables identification of critical residues, whereas the inter-interaction-type-aware layer facilitates the detection of significant interaction types associated with protein thermal stability. These findings highlight the importance of integrating biologically meaningful heterogeneous interactions with protein structure encoding and offer a robust framework for protein thermal stability prediction.