A Two-Step Synthesis of Covalent Genetically-Encoded Libraries of Peptide-Derived Macrocycles (cGELs) enables use of electrophiles with diverse reactivity
Bibliographic record
Abstract
Genetically-encoded libraries of peptide-derived macrocycles containing electrophile 'warheads' (cGELs) can be used to identify potent and selective covalent ligands for protein targets. Such cGELs are synthesized either by incorporation of unnatural amino acids that display mild electrophiles on their side chains or by chemical post-translational modification (cPTM) of mRNA or phage-displayed peptide libraries. Here we investigate fundamental barriers to the synthesis of cGELs. We observe that a previously reported cPTM that proceeds in neutral-to-basic conditions creates mixtures of regioisomers. The complexity of the resulting mixture scales with the electrophilicity of the warhead used in the linker, with some electrophiles being not suitable for use under basic conditions. In contrast, use of a Knorr-pyrazole cPTM enables attachment of electrophiles in acidic pH, thus preventing unwanted reactions with nucleophilic sidechains. The Electrophile is activated only upon mixture with the desired protein target in neutral pH. We use this approach to generate a cGEL with alkyne-bearing macrocycles and use it to identify covalent macrocyclic ligands for pyruvate kinase 2 (PKM2). Our results suggest that construction of cGELs should be performed in conditions that silence the electrophiles (e.g., acidic environment) to prevent unwanted side reactions. In addition to the Knorr-pyrazole method, many other biocompatible bond-forming processes that proceed in mildly acidic pH are likely to be equally effective in constructing cGELs.
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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 | 0.000 | 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".