secondary-structure

New Graph Neural Network Learns Protein Representations with Secondary Structure and Energy-Filtered Hydrogen Bonds

Researchers propose a secondary-structure-aware graph neural network for protein representation learning. The model augments residue-level node representations with secondary structure assignments and constructs edges from hydrogen-bond interactions filtered by energetic strength. It achieves consistent improvements over existing methods on standard protein benchmarks and offers enhanced biological interpretability.