Identification of small molecule allosteric modulators of 5,10-methylenetetrahydrofolate reductase (MTHFR) by targeting its unique regulatory domain
Bibliographic record
Abstract
The folate and methionine cycles, constituting one-carbon metabolism, are critical pathways for cell survival. Intersecting these two cycles, 5,10-methylenetetrahydrofolate reductase (MTHFR) directs one-carbon units from the folate to methionine cycle, to be exclusively used for methionine and S-adenosylmethionine (AdoMet) synthesis. MTHFR deficiency and upregulation result in diverse disease states, rendering it an attractive drug target. The activity of MTHFR is inhibited by the binding of AdoMet to an allosteric regulatory domain distal to the enzyme's active site, which we have previously identified to constitute a novel fold with a druggable pocket. Here, we screened 162 AdoMet mimetics using differential scanning fluorimetry, and identified 4 compounds that stabilized this regulatory domain. Three compounds were sinefungin analogues, closely related to AdoMet and S-adenosylhomocysteine (AdoHcy). The strongest thermal stabilisation was provided by (S)-SKI-72, a potent inhibitor originally developed for protein arginine methyltransferase 4 (PRMT4). Using surface plasmon resonance, we confirmed that (S)-SKI-72 binds MTHFR via its allosteric domain with nanomolar affinity. Assay of MTHFR activity in the presence of (S)-SKI-72 demonstrates inhibition of purified enzyme with sub-micromolar potency and endogenous MTHFR from HEK293 cell lysate in the low micromolar range, both of which are lower than AdoMet. Nevertheless, unlike AdoMet, (S)-SKI-72 is unable to completely abolish MTHFR activity, even at very high concentrations. Combining binding assays, kinetic characterization and compound docking, this work indicates the regulatory domain of MTHFR can be targeted by small molecules and presents (S)-SKI-72 as an excellent candidate for development of MTHFR inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".