Co-evolution-based prediction of metal-binding sites in proteomes by machine learning

Abstract

Metal ions play various important biological role in proteins including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here, we developed a coevolution28 based pipeline named “MetalNet” to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins which significantly expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an E.coli enzyme lacking structural or sequence homology to any known metalloprotein (PDB ID: 7DCM and 7DCN). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.