Computational investigation of turmeric phytochemicals targeting PTR1 enzyme of Leishmania species
Bibliographic record
Abstract
In this study, we used in silico techniques to identify available parasite treatments, representing a promising therapeutic avenue. Building upon our computational initiatives aimed at discovering natural inhibitors for various target enzymes from parasites causing neglected tropical diseases (NTDs), we present novel findings on three turmeric-derived phytochemicals as inhibitors of Leishmania pteridine reductase I (PTR1) through in silico methodologies. PTR1, a crucial enzyme in the unique folate metabolism of trypanosomatid parasites, holds established therapeutic significance. Employing MOE software, a molecular docking analysis assesses the efficacy of turmeric phytochemicals against Leishmania PTR1. Validation of the docking protocol is confirmed with an RMSD value of 2. Post-docking, compounds displaying notable interactions with critical residues and binding affinities ranging between −6 and −8 kcal/mol are selected for interaction pattern exploration. Testing twelve turmeric phytochemicals, including curcumin, zingiberene, curcumol, curcumenol, eugenol, bisdemethoxycurcumin, tetrahydrocurcumin, tryethylcurcumin, turmerones, turmerin, demethoxycurcumin, and turmeronols, revealed binding affinities ranging from −5.5 to −8 kcal/mol. Notably, curcumin, demethoxycurcumin, and bisdemethoxycurcumin exhibit binding affinities within −6.5 to −8 kcal/mol and establish substantial interactions with catalytic residues. These phytochemicals hold promise as lead structures for rational drug design targeting Leishmania spp. PTR in future applications. This work underscores the potential of these identified phytochemicals in the development of more effective inhibitors, demonstrating their relevance in addressing neglected tropical diseases caused by parasites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".