Density Functional Theory Investigation of As(III) S-Adenosylmethionine Methyltransferase
Bibliographic record
Abstract
Abstract Arsenic is one of the most pervasive environmental toxins. It enters our water and food supply through many different routes, including the burning of fossil fuels, the application of arsenic-based herbicides, and natural sources. Using a density functional theory (DFT) cluster approach, the mechanism of arsenic (III) S-adenosylmethionine methyltransferases and various selenium-containing analogues was investigated. Notably, the methylation of arsenic by arsenic (III) S-adenosylmethionine is proposed to be a way to remove arsenic from contaminated water or soil. From the DFT cluster results, it was found that the selective substitution of the active-site Cys44, Cys72, and Cys174 residues with selenocysteines had a marginal effect on the barrier for CH3 transfer. Specifically, the average Gibbs activation energy was calculated to be only 4.2 kJ mol–1 lower than the Gibbs activation energy of 107.4 kJ mol–1 for the WT enzyme. However, importantly, it was found that with selective mutation, the methylation process becomes considerably more exergonic, where the methylation reaction can be made to be 26.4 kJ mol–1 more exergonic than the reaction catalyzed by the WT enzyme. Therefore, we propose that the selective substitution of the active-site Cys44, Cys72 and Cys174 residues with selenocysteines could make the process of methylation and volatilization more advantageous for bioremediation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".