Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network

Atmospheric methane (CH₄) acts as a key contributor to global warming. As CH₄ is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH₄ (the biosynthesized CH₄ from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH₄ emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F₄₃₀ of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among ~54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study.