Cryptic Binding Site Prediction Using Equivariant Graph Neural Networks with Kolmogorov-Arnold Networks

Cryptic binding sites (CBSs) are crucial functional sites that become accessible following conformational changes by ligand binding. They play a significant role in expanding the scope of druggable targets and revealing the dynamic regulatory mechanisms of proteins. Most existing computational methods rely on holo (ligand-bound) structures and struggle to effectively identify CBSs in the apo (ligand-free) state. Furthermore, these methods fail to account for protein three-dimensional conformational changes and spatial geometric information, often making it difficult to explain the dynamic characteristics of CBS formation.To address these challenges, we present CrypKANet, an innovative multimodal predictive framework for cryptic binding site identification that integrates EGNN, gated attention mechanisms, and Kolmogorov-Arnold Network (KAN). Our framework employs a two-branch design to independently encode geometric and biochemical features: the EGNN branch is dedicated to explicitly capturing three-dimensional spatial restraints and conformational dynamics, whereas the GINE branch strengthens the representation of residue-level chemical interactions and topological connectivity. Finally, the model predicts cryptic binding sites through the KAN classifier. Experimental results on the CBS benchmark dataset demonstrate that CrypKANet outperforms the existing state-of-the-art methods, and exhibits excellent generalization performance on protein-protein interaction sites and ligand binding site tasks.