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

Abstract

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 (Zn2+, Ca2+, Mg2+, Mn2+, Fe3+, Co2+, Cu2+). Our model a..