TextFold: A Geometric Hypergraph Framework for Protein Structure Prediction with Scientific Literature Integration

Accurate protein structure prediction remains a key challenge in computational biology, especially for novel folds and complex interactions. We present TextFold, a framework that integrates geometric hypergraphs with multimodal textual context to enhance protein folding predictions. By modeling higher-order spatial relationships and incorporating scientific insights from PubMed and PubTator Central scientific database, TextFold improves accuracy and interpretability through feature attribution analysis. Evaluated on PDB and AlphaFold Protein Structure Database, TextFold achieves a TM-score of 0.81 and an RMSD of 2.1 Å for homologous and 3.1 Å for low-homology proteins, outperforming DeepFold, AlphaFold, and RoseTTAFold in low-homology settings. Ablation studies demonstrate the impact of textual embeddings on prediction refinement. By integrating geometric modeling with domain knowledge, TextFold advances protein structure prediction, offering a valuable tool for drug discovery and functional genomics.