Multivalent ion binding site identification with structure-based deep learning

Abstract

Protein-ion interactions are essential for many cellular processes, including enzymatic catalysis, signaling, and allosteric regulation. However, mapping ion-binding sites experimentally remains labor-intensive and expensive. Here, we present BiteNetI, a structure-based deep learning model that uses 3D convolutional neural networks to simultaneously localize ion-binding centers and predict binding residues for 14 biologically relevant ions. Trained on a carefully curated dataset of over 10,000 high-resolution protein–ion complexes, in which near-identical binding sites are consistently annotated by transferring ions between homologous structures, BiteNetI shows strong generalization ability across diverse ions within a unified multitask architecture. On two different test benchmarks, BiteNetI achieves state-of-the-art performance compared to existing ion-binding predictors as well as to a more general method, AlphaFold3, when used to predict the entire structure of protein bound to ions. Finally, for physiologically relevant ions such as Ca2+, Na+ and K+, BiteNetI achieves two- to three-fold improvement in accuracy. Structure-based deep learning enables accurate prediction of protein-ion binding sites across 14 biologically relevant ions, supporting comprehensive and large-scale annotation of protein-ion interactions.