Disruption of DYRK1A-induced hyperphosphorylation of amyloid-beta and tau protein in Alzheimer’s disease: An integrative molecular modeling approach
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a neurological disorder caused by the abnormal accumulation of hyperphosphorylated proteins. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a dual phosphorylation enzyme which phosphorylates the amyloid-β (Aβ) and neurofibrillary tangles (NFTs). A high throughput virtual screening approach was applied to screen a library of 98,071 compounds against DYRK1A using different programs including AutoDock Vina, Smina, and idock. Based on the binding affinities, we selected 330 compounds for absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Various pharmacokinetics parameters were predicted using the admetSAR server, and based on the pharmacokinetics results, 14 compounds were selected for cross-docking analysis using AutoDock. Cross-docking analysis revealed four compounds, namely, ZINC3843365 (−11.07 kcal/mol −1 ), ZINC2123081 (−10.93 kcal/mol −1 ), ZINC5220992 (−10.63 kcal/mol −1 ), and ZINC68569602 (−10.35 kcal/mol −1 ), which had the highest negative affinity scores compared to the 10 other molecules analyzed. Density functional theory (DFT) analysis was conducted for all the four top-ranked compounds. The molecular interaction stability of these four compounds with DYRK1A has been evaluated using molecular dynamics (MD) simulations on 100 nanoseconds followed by principal component analysis (PCA) and binding free energy calculations. The Gibbs free energy landscape analysis suggested the metastable state and folding pattern of selected docking complexes. Based on the present study outcome, we propose four antagonists, viz., ZINC3843365, ZINC2123081, ZINC5220992, and ZINC68569602 as potential inhibitors against DYRK1A and to reduce the amyloid-β and neurofibrillary tangle burden. These screened molecules can be further investigated using a number of in vitro and in vivo experiments.
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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 | 0.001 | 0.001 |
| 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".