Anti-Methanogenic Effect of Phytochemicals on Methyl-Coenzyme M Reductase—Potential: In Silico and Molecular Docking Studies for Environmental Protection
Bibliographic record
Abstract
Methane is a greenhouse gas which poses a great threat to life on earth as its emissions directly contribute to global warming and methane has a 28-fold higher warming potential over that of carbon dioxide. Ruminants have been identified as a major source of methane emission as a result of methanogenesis by their respective gut microbiomes. Various plants produce highly bioactive compounds which can be investigated to find a potential inhibitor of methyl-coenzyme M reductase (the target protein for methanogenesis). To speed up the process and to limit the use of laboratory resources, the present study uses an in-silico molecular docking approach to explore the anti-methanogenic properties of phytochemicals from Cymbopogon citratus, Origanum vulgare, Lavandula officinalis, Cinnamomum zeylanicum, Piper betle, Cuminum cyminum, Ocimum gratissimum, Salvia sclarea, Allium sativum, Rosmarinus officinalis and Thymus vulgaris. A total of 168 compounds from 11 plants were virtually screened. Finally, 25 scrutinized compounds were evaluated against methyl-coenzyme M reductase (MCR) protein using the AutoDock 4.0 program. In conclusion, the study identified 21 out of 25 compounds against inhibition of the MCR protein. Particularly, five compounds: rosmarinic acid (−10.71 kcal/mol), biotin (−9.38 kcal/mol), α-cadinol (−8.16 kcal/mol), (3R,3aS,6R,6aR)-3-(2H-1,3-benzodioxol-4-yl)-6-(2H-1,3-benzodioxol-5-yl)-hexahydrofuro[3,4-c]furan-1-one (−12.21 kcal/mol), and 2,4,7,9-tetramethyl-5decyn4,7diol (−9.02 kcal/mol) showed higher binding energy towards the MCR protein. In turn, these compounds have potential utility as rumen methanogenic inhibitors in the proposed methane inhibitor program. Ultimately, molecular dynamics simulations of rosmarinic acid and (3R,3aS,6R,6aR)-3-(2H-1,3-benzodioxol-4-yl)-6-(2H-1,3-benzodioxol-5-yl)-hexahydrofuro[3,4-c]furan-1-one yielded the best possible interaction and stability with the active site of 5A8K protein for 20 ns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".