Mapping Peptide–Protein Interactions by Amine-Reactive Cleavable Photoaffinity Reagents
Bibliographic record
Abstract
Photoaffinity labeling followed by tandem mass spectrometry is an often used strategy to identify protein targets of small-molecule drugs or drug candidates, which, under ideal conditions, enables the identification of the actual drug binding site. In the case of bioactive peptides, however, identifying the distinct binding site is hampered because of complex fragmentation patterns during tandem mass spectrometry. We here report the development and use of small cleavable photoaffinity reagents that allow functionalization of bioactive peptides for light-induced covalent binding to their protein targets. Upon cleavage of the covalently linked peptide drug, a chemical remnant of a defined mass remains on the bound amino acid, which is then used to unambiguously identify the drug binding site. Applying our approach to known peptide-drug/protein pairs with reported crystal structures, such as the calmodulin-melittin interaction, we were able to validate the identified binding sites based on structural models. Overall, our cleavable photoaffinity labeling strategy represents a powerful tool to enable the identification of protein targets and specific binding sites of a wide variety of bioactive peptides in the future.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".