Installation of cysteine-derived methyllysine mimics on phage-dis- played peptide libraries: optimization of reaction conditions for conversion and phage viability
Bibliographic record
Abstract
We report a synthetic methodology for the installation of methyllysine mimics on cysteine-containing peptides and bacteriophage peptide libraries. Strategies that allow for diversity and high throughput screening of PTM-containing peptides are critical for successfully targeting the many methyllysine reader proteins that are misregulated in cancer and disease. We have de- veloped conditions for alkylation of cysteine containing peptides with (2-haloethyl) amines, providing products that closely mimic methyllysine residues. Extensive optimization on C7C peptide phage constructs allowed for the successful installation of Kme3 mimics in 60–70% yields to create post-translational ε-Lys-N-methylated peptide phage libraries. Optimized reaction conditions between 2-bromo-N,N,N-trimethylethaninium bromide and commercially available PhD C7C library produce >2 × 1011 phage parti- cles and libraries of ~2 × 108 diversity in which each peptide sequence contains the Kme3 mimic. This process adds a new fragment into readily available genetically encoded libraries and opens new avenues for high throughput screening that may give rise to new ligands for a variety of methyllysine reader proteins.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".