First identification of trigonelline in Guaiacum Officinale fruit and its dengue protease inhibition activity
Bibliographic record
Abstract
Although Dengue virus (DENV) continues to cause substantial global morbidity, no direct-acting antivirals are available for routine clinical use despite the viral NS2B–NS3 serine protease being a well-validated enzymatic target for small-molecule inhibition. Here we report, to our knowledge, the first identification (and HPLC quantification) of trigonelline (N-methylnicotinic acid) in Guaiacum officinale fruit and demonstrate its inhibition of the DENV-2 NS2B–NS3 protease (IC₅₀ = 76±3 μM). To explain this activity, we integrated unbiased molecular docking with molecular dynamics (MD) and end-point calculations. The MD-relaxed binding mode places the trigonelline carboxylate in a polar niche stabilized by a Ser135 and Tyr150 hydrogen-bond dyad, while the pyridinium ring engages an adjacent aromatic shelf (Tyr161 and Phe130) near the His51–Asp75–Ser135 catalytic triad. The low protein RMSD and low ligand drift suggest this is a persistent pose. MM/GBSA calculated affinity is dominated by van der Waals contributions, and a per-residue decomposition highlight stabilizing roles for Ser135, Tyr150, and Pro132, consistent with the observed contact pattern and supportive of target engagement. While the potency is moderate, it is consistent with a fragment-like chemotype in the protease substrate-binding cleft and provides a tractable entry point for derivatization. Contact-persistence and the energy decomposition suggest promising avenues for future structure–activity exploration, i.e., extension of the ligand into the S1/S2 subsites, reinforcement of aromatic contacts, and polarity management to increase hydrophobic collapse as a new opportunity for dengue protease inhibitor design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".