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

Metal-binding proteins account for nearly half of the characterized proteome, and they rely on metal-binding sites (MBSs) as critical determinants of their structural stability and biological function. However, 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. Author summaryWe study how metals shape protein structure and function by comparing metal-binding sites (MBSs) rather than whole proteins. We represent each MBS site as a point cloud of atoms surrounding the bound metal and align 23,342 sites from the Protein Data Bank (PDB) with a fine-tuned iterative closest point algorithm. This yields a similarity network whose links mirror metal coordination chemistry and enzymatic roles: sites binding the same metal or sharing enzyme classes cluster together, and conserved metalloenzyme families (e.g., binuclear ureohydrolases) form tight subnetworks that also capture atypical members such as a dinickel metformin hydrolase. Because highly similar MBS geometries often link proteins with low sequence identity, the MBS network highlights candidates consistent with either divergent evolution with locally conserved MBS architecture or convergent evolution toward similar coordination geometries in otherwise unrelated protein contexts. Overlaying known drug-binding sites lets us flag drugs whose targets are tightly connected and propose plausible off-targets, recovering known matrix metalloproteinase off-targets and suggesting new ones. Our approach offers a scalable map of metalloprotein relationships useful for studying evolution and anticipating drug cross-reactivity.