mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding

Metal ions play critical structural, regulatory, and enzymatic roles in proteins, making their binding essential for biological processes. Experimental identification of metal-binding sites is resource-intensive and limited in scalability. Recent advances in protein language models have transformed computational predictions, yet current tools do not address how residue-level metal-binding probabilities change upon mutation. To fill this gap, mCSM-metal leverages embeddings from ESMBind with our graph-based structural signatures to accurately predict the effects of single or multiple point mutations on the binding of seven essential ions (Zn 2+ , Ca 2+ , Mg 2+ , Mn 2+ , Fe 3+ , Co 2+ , Cu 2+ ). Our model achieves accuracies, F1-scores, and Matthews Correlation Coefficient values up to 0.97, 0.97, and 0.95, outperforming other approaches. The webserver provides an interactive platform to assess and visualize local and long-range impacts of mutations on metal-ion binding, offering new avenues for applications in structural biology, disease modelling, and protein engineering. The web application is freely available at: https://biosig.lab.uq.edu.au/mcsm_metal/ .