Computational Study of Triterpenoids of Ganoderma lucidum with Aspartic Protease Enzymes for Discovering HIV-1 and Plasmepsin Inhibitors
Bibliographic record
Abstract
Rapid resistance development of HIV-1 and Plasmodium falciparum parasite requires discovery of more potent new drugs. Aspartic protease enzymes expressed by HIV-1 and P. falciparum could be used as important drug targets. The catalytic site is located at the bottom of a cleft in the enzyme surface and consists of triad Asp25, Thr26, Gly27. Important aspartic acids are Asp32 and Asp215. Aspartic proteases are inhibited by pepstatin-A, a naturally occurring peptide containing two statins, which replace the amino acids. The hydroxyl group of the statin binds tightly to the catalytically-active aspartic acid residues in the active site of protease, thereby mimicking the transition state of the peptide cleavage. Previous study proved that ganoderiol-F, a triterpenoid isolated from the stem of Ganoderma sinense showed higher affinity towards HIV-1 protease (binding energy= -11.40 kcal/mol and Ki= 4.68 nM) than to plasmepsin I (binding energy= -9.96 kcal/mol and Ki= 50.94 nM). In this paper, computational studies of G. lucidum triterpenoids with aspartic protease enzymes of HIV-1 and plasmepsin I, were performed using AutoDock 4.2. Nelfinavir and KNI-10006 were used as the standards for HIV-1 protease and plasmepsin I, respectively. The four triterpenoids are able to interact with both enzymes. Ganoderat acid-B showed the best affinity to HIV-1 protease (binding energy= -7.49 kcal/mol and Ki= 0.001 mM) which is better than nelfinavir. Furthermore, the best affinity to Plasmepsin I is showed by ganodermanondiol (binding energy= -7.14 kcal/mol and Ki= 0.005 mM which is better than KNI-10006. According to the values of binding energy and inhibition constant, triterpenoids of G. lucidum could be developed further as both anti-HIV and anti-malaria.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".