Discovery of potent inhibitors of rumen methane metabolism via an AI-assisted workflow
摘要
Ruminant methane emission has attracted increasing attention due to its significance in global climate change and sustainable livestock production. Methyl-coenzyme M reductase (MCR), the key enzyme involved in the terminal step of methanogenesis in the rumen microbiome, is responsible for nearly all biologically generated methane released into the atmosphere. In this study, we developed an AI-assisted workflow for discovery of novel MCR inhibitors. Ultra-fast virtual screening of ~23 million molecules yielded 26 candidate molecules for experimental validation, among them 4 molecules showed significant inhibition of methane metabolism. Subsequent structure-guided optimization led to the discovery of a sub-nanomolar inhibitor Pyrazol-5(4H)-one,3-(4-nitrophenyl) (PZON), which effectively suppressed methane production in rumen microbiome fermentation and exhibited minimal cytotoxicity. Overall, our study provides promising candidates for methane-reducing feed additives, and demonstrates the power of AI-assisted discovery of small molecules for targeted modulation of gastrointestinal microbiomes.
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