<i>De</i> <i>novo</i> helical peptides as target sequences for a specific, fluorogenic proteinlabelling strategy
Bibliographic record
Abstract
New methods are needed to selectively label proteins in a manner that minimally perturbs their structures and functions. We have developed a 'small molecule'-based labelling technique that relies on the use of dimaleimide fluorogens that react with a target peptide sequence that presents appropriately spaced, solvent-exposed Cys residues. The thiol addition reaction between target sequence and dimaleimide fluorogen restores the latent fluorescence of the latter and results in the covalent fluorescent labelling of the protein of interest (J. Guy, K. Caron, S. Dufresne, S. W. Michnick, W. G. Skene and J. W. Keillor, J. Am. Chem. Soc., 2007, 129, 11969-11977). We demonstrated the proof-of-principle of this method previously, using a dicysteine mutant of the helical protein Fos (S. Girouard, M.-H. Houle, A. Grandbois, J. W. Keillor and S. W. Michnick, J. Am. Chem. Soc., 2005, 127, 559-566). Herein, we present the design of a novel peptide sequence presenting two Cys residues separated by two turns of an alpha-helix. The secondary structure of this sequence was confirmed by CD spectroscopy, before and after the fluorescent labelling reaction. A new series of di(3-methylmaleimide) fluorogens was prepared and kinetically evaluated, tuning their reactivity toward the target sequence. Attempts were made to increase the reactivity of the parent target sequence by rational design; however, the introduction of basic His residues in the vicinity of one or more Cys residues did not have the desired effect. Finally, epidermal growth factor receptors bearing the de novo target sequence were specifically labelled with a di(3-methylmaleimide) fluorescein fluorogen, validating our method for specific cell-surface labelling of proteins. A wide variety of fluorogen and peptide designs can be envisioned with potential applications to multiplexed labelling for the study of temporal and spatial dynamics of protein expression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".