Differential binding of copper and zinc to a TDP-43 RNA recognition motif decapeptide and disulfide formation at residues C173/5 revealed by ESI-MS/MS
Bibliographic record
Abstract
Copper (Cu) and zinc (Zn) metal ions play important roles in the proper functioning and localization of neurological proteins, such as transactive response DNA-binding protein 43 (TDP-43), which is linked to amyotrophic lateral sclerosis (ALS). Previous experimental and computational studies have identified putative Zn-binding regions within the RNA recognition motif 1 (RRM1) of TDP-43. However, Cu-binding interactions have been less explored despite their redox activity in regulating thiol (C173/175) conversion to disulfide within the RRM1 domain, influencing protein structure and function. Herein, the structural characterization and fragmentation pattern analysis of a TDP-43 decapeptide (166-HMIDGRWCDC-175), within RRM1, coordinated to Cu(II) and Zn(II) ions using electrospray ionization tandem mass spectrometry (ESI-MS/MS) was conducted under non-denaturing conditions. Higher-energy collision dissociation (HCD) fragmentation analysis identified that Cu(II) prefers His/Met residues, while Zn(II) was weakly coordinated to various binding sites in the peptide, specifically His, Met, Glu, Cys, Trp and Asp residues. Computational modeling using a metal ion binding server (MIB2) confirmed the binding sites and coordination sphere of metal-peptide complexes. No significant coordination to C173 and C175 was observed with Cu or Zn, as identified by using a double Cys mutant peptide. A complete thiol-to-disulfide conversion was observed in the presence of Cu(II)/(I) only, which was confirmed by the comparison of a preformed intramolecular disulfide peptide. Overall, unique differential coordination environments were observed for each metal ion with the peptide. The study provides new insights into metal ion interactions with TDP-43 RRM1 peptide, leading to a greater understanding of metal homeostasis in TDP-43 protein biochemistry and neurodegeneration.
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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".