The effect of mutation on neurotoxicity reduction of new chimeric reteplase, a computational study
Bibliographic record
Abstract
Background and purpose: Excitotoxicity in nerve cells is a type of neurotoxicity in which excessive stimulation of receptors (such as N-methyl-d-aspartate glutamate receptors (NMDAR)) leads to the influx of high-level calcium ions into cells and finally cell damage or death. This complication can occur after taking some of the plasminogen activators like tissue plasminogen activator and reteplase. The interaction of the kringle2 domain in such plasminogen activator with the amino-terminal domain (ATD) of the NR1 subunit of NMDAR finally leads to excitotoxicity. In this study, we assessed the interaction of two new chimeric reteplase, mutated in the kringle2 domain, with ATD and compared the interaction of wild-type reteplase with ATD, computationally. Experimental approach: Homology modeling, protein docking, molecular dynamic simulation, and molecular dynamics trajectory analysis were used for the assessment of this interaction. Findings/Results: The results of the free energy analysis between reteplase and ATD (wild reteplase: -2127.516 ± 0.0, M1-chr: -1761.510 ± 0.0, M2-chr: -521.908 ± 0.0) showed lower interaction of this chimeric reteplase with ATD compared to the wild type. Conclusion and implications: The decreased interaction between two chimeric reteplase and ATD of NR1 subunit in NMDAR which leads to lower neurotoxicity related to these drugs, can be the start of a way to conduct more tests and if the results confirm this feature, they can be considered potential drugs in acute ischemic stroke treatment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".