MOTIVATION: In recent years, protein language models (pLMs) and graph neural networks (GNNs) have demonstrated powerful expressive and reasoning capabilities in modeling protein-RNA/DNA interactions. However, existing methods, which use simple graphs to describe the relationships between residues, struggle to effectively capture the high-order, multi-body residue interactions present in protein-nucleic acid complex structures. In fact, spatially continuous but sequence-wise discontinuous residues often cooperatively determine nucleic acid binding capacity. RESULTS: In this study, we present IKANbind, a computational approach that combines hypergraph representation learning and interpretable Kolmogorov-Arnold Networks (KANs), for identifying nucleic acid binding residues (NBRs) in proteins. By combining the advantages of pLM, hypergraph neural networks and symbolic KAN, IKANbind outperforms existing methods on multiple NBR benchmark datasets. We also demonstrated that the pLM used in IKANbind can implicitly learn the physicochemical properties of binding residues, such as charge and hydrophobicity. In addition, the symbolic KAN, which uses a unique weighted mechanism of decomposable basis functions, can accurately identify the features with the greatest contribution to NBR recognition. We found that polarity and charge make greater contributions to NBR prediction than other physicochemical properties or evolutionary information. Finally, IKANbind achieves promising performance when extended to other ligand-binding residue prediction tasks. AVAILABILITY AND IMPLEMENTATION: IKANbind is freely available at https://github.com/yangfengzhuguet/IKANBind.

Accurately identifying miRNA-disease association (MDA) is of great importance in biomedical research and clinical applications. However, most existing computational methods rely on similarity, making it difficult to effectively capture the deep semantic information among heterogeneous nodes in complex networks. In addition, the inherent "closed box" nature of traditional deep learning models leads to a lack of transparency in their decision-making process. Therefore, we propose an interpretable MDA prediction method (iDRKAN) based on dual-graph representation learning and Kolmogorov-Arnold Network. First, iDRKAN constructs similarity views and meta-path views based on the similarity matrix and association matrix respectively, and learns higher-order feature representation of each view by graph convolutional network (GCN). Subsequently, the multi-channel attention (MCA) mechanism is introduced to adaptively fuse the contextual information of each similarity view in different convolutional layers, and the semantic layer attention (SLA) mechanism is used to integrate the semantic information contained in different meta-path views. Next, the contrastive learning strategy is used to optimize the consistency between the dual-graph representation. Finally, the dual-graph features are weighted fused, and fed into the interpretable Kolmogorov-Arnold Network (KAN) for prediction. Experimental results on two public datasets show that iDRKAN significantly outperforms existing computational approaches in multiple performance indicators. In addition, experiments with different classifiers verify that iDRKAN achieves a good balance between prediction performance and interpretability. The case study further demonstrates the effectiveness of iDRKAN in mining potential associations.

BACKGROUND: Protein-protein interactions regulate the dynamic operation of intracellular molecular networks, serving as the molecular basis for revealing protein functions and disease mechanisms. Recently, several computational methods for predicting protein-protein interaction sites (PPIs) have been presented as alternatives to costly and labor-intensive traditional experiments. However, existing methods generally ignore the inherent hierarchical structure of protein chains. Furthermore, the equivariance of graph structure during spatial transformations is often neglected when applying graph neural networks to modeling. Therefore, accurately identifying PPIs remains a challenging task. RESULTS: In this work, we propose an end-to-end GNN-based computational method, EGCPPIS, for efficiently identifying protein-protein interaction sites. First, we construct a hierarchical graph representation of the protein chain, including residue-level graph and atom-level graph. Next, EGCPPIS designs an E(n) Equivariant Graph Neural Network (EGNN) module to learn residue-level embeddings with equivariant features. After further extracting atom-level embeddings using the GraphSAGE module, we introduce the contrastive learning strategy to integrate hierarchical graph features. This strategy enables us to learn consistent embeddings between residue-level and atom-level representations. Finally, the fused embeddings are weighted using an improved gated multi-head attention mechanism. CONCLUSION: Comprehensive evaluation results on multiple datasets demonstrate that EGCPPIS significantly outperforms state-of-the-art methods. Extensive comparative experiments and case studies further confirm that EGCPPIS can reveal the decision-making patterns in PPIs prediction, facilitating the discovery of potential PPIs. The original datasets and code of EGCPPIS are available at https://github.com/GuicongSun/EGCPPIS .