ZHMolTopoRPI: A Commutative Algebra-Driven Deep Learning Framework for Robust RNA-Protein Interaction Prediction

Accurate prediction of RNA-protein interactions (RPIs) is crucial for understanding post-transcriptional regulation. Most existing models depend on implicit embeddings within large language models, which limits their physical interpretability. This study introduces ZHMolTopoRPI, a computational framework that integrates persistent commutative algebra with dual-tower neural networks. We employ the persistent Stanley-Reisner theory (PSRT) to extract multiscale mathematical features from RNA sequences. For feature fusion and RPI prediction, we use a contrastive learning-enhanced gated attention dual-tower network (CL-GADTN) to combine RNA features with protein semantic information from ESM2. Evaluation on six benchmark data sets (NPInter2, RPI7317, RPI488, RPI1807, RPI2241, and NPInter v2.0) demonstrated MCC scores of 92.29, 84.83, 81.55, 88.42, 89.23, and 92.63%, respectively. Additionally, the model's analysis of commutative algebra perturbations revealed motif changes caused by pathogenic single nucleotide polymorphisms (SNPs). We further validated ZHMolTopoRPI's practical utility in functional target screening of the human proteome. Overall, it offers a quantitative and interpretable approach for precise RPI prediction.