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Microbes and Methanogenesis01:26

Microbes and Methanogenesis

Methanogenesis is a critical microbial process in anaerobic ecosystems responsible for the biological production of methane, a potent greenhouse gas and valuable biofuel. This metabolic pathway is primarily facilitated by methanogenic archaea, which thrive in anoxic environments such as wetlands, sediments, and animal gastrointestinal tracts. The absence of oxygen in these habitats prevents aerobic respiration, thereby favoring alternative biochemical pathways for organic matter degradation.In...

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Related Experiment Video

Updated: May 7, 2026

Continuous Fluorescence-Based Endonuclease-Coupled DNA Methylation Assay to Screen for DNA Methyltransferase Inhibitors
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Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network.

Randy Aryee1,2, Noor S Mohammed1,2, Supantha Dey1

  • 1Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.

Biorxiv : the Preprint Server for Biology
|September 30, 2024
PubMed
Summary

Reducing enteric methane (CH4) emissions is crucial for climate change mitigation. This study details molecular interactions of inhibitors with methyl coenzyme M reductase and uses AI to discover new methane-reducing compounds.

Keywords:
bromoformclimate changeemissions mitigationenteric methanogenesisenzyme inhibition

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Area of Science:

  • Biochemistry
  • Environmental Science
  • Computational Chemistry

Background:

  • Methane (CH4) is a potent greenhouse gas, with enteric fermentation in ruminants contributing significantly to global CH4 emissions.
  • Mitigating short-lived climate forcers like CH4 is essential for near-term climate change management.
  • Understanding methanogenesis inhibitors is key to reducing agricultural greenhouse gas output.

Purpose of the Study:

  • To elucidate the biophysical and thermodynamic interactions between anti-methanogenic molecules and the cofactor F430 of methyl coenzyme M reductase.
  • To computationally identify novel precursors and inhibitors of methanogenesis.
  • To establish a foundation for the rational design of new methane-reducing agents.

Main Methods:

  • Detailed biophysical and thermodynamic analysis of inhibitor-cofactor F430 interactions.
  • Graph neural network modeling to functionally cluster known inhibitors among bovine metabolites.
  • Prediction of chemical similarity and membrane permeability for identifying novel inhibitors.

Main Results:

  • Characterization of binding affinities and stoichiometric ratios for sixteen known methanogenesis inhibitors.
  • Functional clustering of inhibitors within a large bovine metabolite dataset using a graph neural network.
  • Development of a protocol for identifying potential methanogenesis inhibitors based on chemical properties.

Conclusions:

  • The study provides a mechanistic understanding of anti-methanogenic molecule interactions.
  • A computational framework is established for discovering novel inhibitors of enteric methane production.
  • This research facilitates the computational and de novo design of targeted methane mitigation strategies.