Advancing metalloproteome analysis by chemical and computational proteomics

Metal-binding proteins constitute approximately more than one-third of the proteome and perform essential roles across all kingdoms of life. Consequently, dysregulation of metal homeostasis is intricately linked to diverse pathologies. The development of robust methodologies for comprehensive characterization of the metalloproteome is crucial for discovering novel metalloproteins and elucidating their functions in physiological and pathological processes. Here, we review recent advancements in metalloproteomics, tracing technological evolutions from leveraging thermodynamic stability for endogenous metalloprotein discovery to mapping local structural rigidity at the peptide level, and ultimately achieving single-residue resolution through activity-based probes. In parallel, we highlight computational strategies for mining metalloproteomes from sequence-based methods using protein language models to structure-based graph neural networks, and to the generative co-folding of holo-complexes. Finally, we discuss how integrating chemical and computational proteomics may facilitate site-resolved characterization and generate testable hypotheses for mechanistic interpretation and functional annotation of the unknown metalloproteins.