Caffeine and Nicotine with N-Substituted Diazirine Photoaffinity Labels Form Adducts at Tyrosine-39 of α-Synuclein
Bibliographic record
Abstract
Aggregates of the protein α-synuclein are found in Lewy bodies in the brains of Parkinson’s disease (PD) patients. Small molecules that can attenuate or halt α-synuclein aggregation have been studied as potential therapeutics for PD. However, we have a limited understanding of how these molecules bind to α-synuclein. We previously identified that caffeine, nicotine, and 1-aminoindan all bind to both the N- and C-terminus of α-synuclein, although the binding location remains unknown. In an effort to identify these binding regions on α-synuclein, we synthesized diazirine photoaffinity probes attached to caffeine (C-Dz), nicotine (N-Dz), and 1-aminoindan (I-Dz) and allowed each to react with α-synuclein in vitro . We then treated the incubation mixture with trypsin and employed time-of-flight mass spectrometry to analyze the resulting peptides. Our findings reveal a distinctive binding pattern among the probes: C–Dz forms covalent bonds with Tyr-39 and Glu-20, while N-Dz selectively forms a covalent bond with Tyr-39. Intriguingly, we could not detect the labeling of I-Dz to any specific amino acids. All of the diazirine-bound peptides were found near the N-terminus. Our results suggest that the N-terminal region near Tyr-39 bears further study to elucidate the binding interactions of small molecules with α-synuclein and may be a target for anti-PD agents.
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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 | 0.001 |
| 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 | 0.001 |
| 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".