Computational analysis and drug repositioning: Targeting the TDP-43 RRM using FDA-approved drugs
Bibliographic record
Abstract
TAR DNA binding protein-43 (TDP-43) aggregation is a hallmark for many neurodegenerative diseases including amyotrophic lateral sclerosis, frontotemporal dementia and Huntington’s disease. These protein aggregates can disrupt neuronal function and contribute to neurodegeneration. Previous studies have uncovered that adenosine triphosphate (ATP) is a promising molecule to dock onto the TDP-43 RNA recognition motif (RRM) region to reduce amyloid-like aggregation. This provides a potential therapeutic strategy in which chemicals with similar binding properties could be selected as drugs. Under normal physiological conditions, TDP-43 RRM region mediates the binding of nucleic acid with TDP- 43. Therefore, we hypothesized that molecules such as tyrosine kinase inhibitors (TKIs), which can bind to ATP-binding sites or competitively bind to other nucleic acid binding regions, including different variants of RRM domains, are of great screening interest. We conducted in silico simulations using molecular dynamic simulation and virtual screening, in which the ATP-binding pocket is introduced in docking model. Our results supported our hypothesis because five of ten selected binding chemicals were TKIs. From the result, we then selected the two molecules under maximum concentration in bloodstream by conducting further screening strategies such as long-term molecular dynamic simulation, and Lipinski’s rules testing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".