Discovery of potent inhibitors of rumen methane metabolism via an AI-assisted workflow

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 production. Subsequent structure-guided optimization led to the discovery of a nanomolar-range inhibitor P yra z ol-5(4H)- o ne,3-(4- n itrophenyl) (PZON) with an IC 50 of 0.24 μM. PZON inhibited Methanobrevibacter ruminantium M1 growth and methanogenesis in pure cultures, and suppressed methane emissions by 73–98% across rumen microbiomes from four ruminant sources, while exhibiting no detectable cytotoxicity at concentrations exceeding 400-fold its IC 50 . Metatranscriptomic analyses further demonstrated that PZON reduced transcriptional activity of diverse methanogenic lineages and MCR, providing transcriptional evidence of the anti-methanogenic activity of PZON. 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.