Rondon, J. J.; Antelo, G. T.; Capdevila, D. A.

Metal ions play essential roles in bacterial physiology, acting as cofactors and signals for transcriptional responses under stress conditions. To maintain metal homeostasis, bacteria rely on specialized metalloregulatory transcription factors, including representatives of the ArsR, MerR, Fur, MarR, and other families. Although these regulators have been extensively studied in a handful of model organisms, the vast majority of their homologs across bacterial diversity remain uncharacterized. This gap limits functional annotation in comparative genomics and the ability to identify novel sensors in pathogens or environmental species. Sequence Similarity Networks (SSNs) offer a scalable approach to visualize and analyze protein families with thousands of sequences, enabling the identification of putative functional clusters based on sequence divergence. Integrating SSNs with curated annotations provides an opportunity to evaluate the current state of knowledge within each family, quantify the number of regulators with known inducers, and identify clusters that remain unexplored. Such quantitative assessments help prioritize families or clades for deeper functional investigation. Beyond descriptive analyses, SSNs can inform the construction of cluster-specific profile Hidden Markov Models (HMMs), which allow sensitive detection of candidate sensors in bacterial genomes. This is particularly relevant for non-model organisms, where gene annotation is often limited. By combining SSNs, HMM-based genomic mining, phylogenetics, and a structural approach, we aim to propose a unified framework for mapping the functional landscape of metalloregulatory families and discovering previously unrecognized sensors.