HiPHD: Hierarchical Classification for Protein Remote Homology Detection by Incorporating Protein Sequential and Structural Information

Protein remote homology detection is crucial in various biological tasks, such as protein function annotation and structure prediction. Computational methods have been developed to improve efficiency and accuracy of protein homology detection. However, proteins with remote homology usually share similar structures and low sequence identity, resulting in limited performance of sequence/structure alignment-based methods. This study introduces HiPHD, a hierarchical classification framework for protein remote homology detection. HiPHD integrates protein sequential information embedded by protein language models and structural information encoded by graph neural networks, effectively combining spatial and sequential features. Experimental results demonstrate that HiPHD outperforms existing methods in terms of accuracy at all hierarchical levels in both SCOPe and CATH databases. It's anticipated HiPHD will become a valuable tool for protein homology detection and representation learning.