Fast and accurate detection of metal resistance genes using MetHMMDb
Heavy metal pollution poses a major environmental challenge, with microbial resistance to heavy metals offering potential solutions through bioremediation. Additionally, the presence and diversity of microbial metal resistance genes (MMRGs) could contribute to an ecosystems ability to adapt and recover from heavy metal contamination by maintaining essential microbial functions and promoting the cycling of nutrients under stress conditions. Thus, MMRGs may serve not only as markers of contamination but also as indicators of an ecosystems self-purification capacity and resilience to environmental disturbances. Here we present MetHMMDB, a database containing 254 profile Hidden Markov Models representing 121 MMRGs. Unlike traditional sequence-based resources, MetHMMDB relies on HMMs to improve detection sensitivity and functional specificity across microbial communities. Created through iterative database searches, sequence clustering, structural prediction, and manual annotation, MetHMMDB emphasizes functional annotation rather than gene classification. The database outperforms sequence-based approaches, identifying over twice as many MMRGs in metagenomic datasets, including those from extreme environments. Analysis of agricultural soil revealed distinct resistance profiles correlating with soil quality. MetHMMDB advances our understanding of microbial adaptation to heavy metal contamination while supporting environmental management strategies through improved identification and characterization of metal resistance mechanisms. Database URL: https://github.com/Haelmorn/MetHMMDB.