This research introduces a novel bidirectional hierarchical framework that integrates both protein sequence and structural information for enhanced biological predictions.

• Combines strengths of protein language models (pLMs) with graph neural networks (GNNs) to capture complete protein characteristics
• Implements innovative hierarchical fusion to bridge sequence and structural representations
• Demonstrates superior performance across enzyme classification, protein-ligand binding, and protein-protein interaction tasks
• Establishes a new state-of-the-art approach for multi-modal protein representation

Why It Matters: This breakthrough enables more accurate protein function prediction critical for drug discovery, understanding disease mechanisms, and advancing precision medicine applications.

Original Paper: Bidirectional Hierarchical Protein Multi-Modal Representation Learning