Graph deep learning locates magnesium ions in RNA

Magnesium ions (Mg2+) are crucial for RNA structure and cellular functions, yet locating them accurately in RNA has been a challenge. We introduce a machine-learning method, originally designed for computer vision, to predict Mg2+ binding sites in RNA by considering geometric and electrostatic RNA features. Through deep learning, we accurately predict Mg2+ density distribution. Validated with five-fold cross-validation on a dataset of 177 Mg2+-containing structures and compared with other methods, our approach demonstrates significantly improved accuracy and efficiency. Saliency analysis reveals essential coordinating atoms and uncovers new Mg2+ binding motifs. Combining this approach with X-ray crystallography can identify metal ion binding sites in RNA, advancing our understanding of RNA structure and function.