Late-Stage Reshaping of Phage-Displayed Libraries to Macrocyclic and Bicyclic Landscapes using a Multipurpose Linchpin
Bibliographic record
Abstract
Genetically encoded libraries (GEL) are increasingly being used for the discovery of ligands for “undruggable” targets that cannot be addressed with small molecules. Foundational GEL platforms like phage-, yeast-, ribosome-, and mRNA-display have enabled the display of libraries composed of 20 natural amino acids (20AA). Unnatural amino acids (UAA) and chemical post-translational modification (cPTM) expanded GEL beyond the 20AA space to yield unnatural linear, cyclic, and bicyclic peptides. The standard operating procedure incorporates UAA and cPTM into a “naive” library with 10 8 –10 12 compounds and uses a chemically upgraded library in multiple rounds of selection to discover target-binding hits. However, such an approach uses zero knowledge of natural peptide-receptor interactions that might have been discovered in selections performed with 20AA libraries. There is currently no consensus regarding whether “zero-knowledge” naive libraries or libraries with pre-existing knowledge can offer a more effective path to discovery of molecular interactions. In this manuscript, we evaluated the feasibility of discovery of macrocyclic and bicyclic peptides from “nonzero-knowledge” libraries. We approach this problem by late-stage chemical reshaping of a preselected phage-displayed landscape of 20AA binders to NS3aH1 protease. The reshaping is performed using a novel multifunctional C 2 -symmetric linchpin, 3,5-bis(bromomethyl)benzaldehyde (termed KYL), that combines two electrophiles that react with thiols and an aldehyde group that reacts with N-terminal amine. KYL diversified phage-displayed peptides into bicyclic architectures and delineated 2 distinct sequence populations: (i) peptides with the HXDMT motif that retained binding upon bicyclization and (ii) peptides without the HXDMT motif that lost binding once chemically modified. The same HXDMT family can be found in traditional selections starting from the naive KYL-modified library. Our report provides a case study for discovering advanced, chemically upgraded macrocycles and bicycles from libraries with pre-existing knowledge. The results imply that other selection campaigns completed in 20AA space, potentially, can serve for late-stage reshaping and as a starting point for the discovery of advanced peptide-derived ligands.
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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 | 0.000 |
| 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".