Copper ions and the R peptides of tau: structural and functional study
Bibliographic record
Abstract
Abstract Background One of the common neurodegenerative disorders is the aggregation of Tau protein plays a major role in Alzheimer’s disease. Transmission metals ions such as Cu, Zn, and Fe are vital ingredients that must maintain homeostasis in the biological system. Tau Protein has four repeat peptide units (R1 R2 R3 R4) which may coordinate divalent copper ions to form neurofibrillary tangles (NFTs) and generate reactive oxygen species (ROS).The abnormal amount of Cu2+ accumulation in the intracellular environment, regulating the biological systems is not clearly understood. Method The main objective of this study was to evaluate the coordination of four R peptides to Cu2+ by MALDI‐MS‐TOF and determine the interaction of Cu2+ ions with peptides (R1 R2 R3 R4) of Tau protein in vitro. Also, their role in the formation of ROS with a natural reducing agent‐ ascorbate was probed. Result As the principal findings of this experiment, metal ion coordinated all 4 R peptides, via His and Cys residue including N terminal and amide groups. All the metallo‐peptide complexes were singly charged peaks, and observed even in the presence of ascorbate. R2 and R3 peptides formed the homodimers by disulfide bond formation. The metallo‐peptides were antioxidants and prooxidants depending on the conditions, indicating that a complex behavior may exist in the biological setting. Conclusion This work indicates that metal ions interact with R peptides, which may mitigate ROS levels. The finding of this study is greatly beneficial for understanding the Tau protein behavior to develop a potential medication.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".