Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets.

Metal-binding sites (MBSs) are critical determinants of protein stability and biological function, yet methods for comparing their local binding environments lag behind those for whole-structure alignment. Here, we represent MBSs as atomic point clouds surrounding bound metal ligands and align them with a fine-tuned iterative closest point algorithm. Applying this framework to a redundancy-reduced collection of MBSs derived from all metalloproteins in the Protein Data Bank (PDB), we perform pairwise alignments across 23,342 sites to construct a similarity network of metal-binding environments. The resulting network topology recapitulates metal coordination chemistry and enzyme function: links are strongly enriched within metal types and across shared EC subclasses. Conserved metalloenzyme families form cohesive subnetworks; for example, the binuclear ureohydrolase domain appears as two tightly connected components that also capture atypical members such as the dinickel metformin hydrolase. We observe only a moderate global association between protein sequence and MBS geometry, yet many network links connect near-identical binding-site architectures across proteins with low sequence identity, consistent with either divergent evolution with local MBS conservation or candidate cases of molecular convergent evolution. Integrating network proximity with structural evidence of drug binding identifies drugs with enriched connectivity among their targets and predicts 528 drug-off-target combinations across 88 drugs and 151 human proteins, recovering both known off-targets (e.g., ADAM/ADAMTS for matrix metalloproteinase inhibitors) and proposing novel ones. The MBS network thus provides a scalable resource for probing metalloprotein evolution, functional convergence, and the structural basis of drug cross-reactivity.