In silico profiling of neem limonoids and gut microbiome metabolites for Alzheimer’s therapeutics: targeted inhibition of BACE1 and elucidation of intricate molecular crosstalk with tau oligomers, and bacterial gingipains
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is characterized by the accumulation of amyloid beta plaques and neurofibrillary tangles composed of hyperphosphorylated tau protein. This study computationally investigated natural neem compounds (limonoids) and gut microbiome metabolites for their inhibitory potential against key AD targets. Molecular docking analyses were performed on approximately 200 neem phytochemicals and 9 microbial metabolites against beta-secretase 1 (BACE1), gingipain cysteine protease, and tau oligomerization receptors using AutoDock. BBB permeability was computationally evaluated using six molecular descriptors: molecular weight, LogP, hydrogen bond acceptors/donors, polar surface area, and rotatable bonds, categorizing compounds as highly or poorly BBB permeable based on established predictive criteria. The results revealed superior binding affinities of limonoids, notably Rutin (− 9.642 kcal/mol), 7-benzoylnimbocinol (− 9.706 kcal/mol), and tirucallol (− 9.488 kcal/mol) against BACE1, gingipain protease, and tau oligomerization receptors, respectively. These compounds exhibited key interactions through hydrogen bonding with Gly34, Asn233 (rutin-BACE1), Lys311, and Asn363 (7-benzoylnimbocinol-gingipain) and hydrophobic interactions with Ile40 and Ile48 (tirucallol-tau). While these limonoids demonstrated binding affinities exceeding melatonin by > 30%, their BBB permeability profiles necessitate sophisticated delivery strategies. Among gut microbiome metabolites, melatonin showed consistent binding across all targets (− 7.079 to − 8.452 kcal/mol). These findings establish limonoids’ superiority over gut microbiome metabolites and highlight their therapeutic potential as multi-target inhibitors in AD pathology, warranting investment in nanocarrier systems for optimizing BBB penetration.
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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 | 0.001 |
| 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".