Late-stage reshaping of phage-displayed libraries to macrocyclic and bicyclic landscapes using multipurpose linchpin
Bibliographic record
Abstract
Genetically-encoded libraries (GEL) are increasingly used for discovery of ligands for ‘undruggable’ targets that cannot be addressed with small molecules. Foundational GEL platforms like phage-, yeast-, ribosome- and mRNA-display enabled display of libraries composed of 20 natural amino acids (20AA). Today, numerous strategies expand GEL beyond 20AA space by incorporating unnatural amino acids (UAA) and chemical post-translational modification (cPTM) to build linear, cyclic, and bicyclic peptides. The standard operating procedure for UAA and cPTM libraries starts from a "naïve" chemically-upgraded library with 108-1012 compounds, uses target of interest and rounds of selection to narrow down to a set of receptor binding hits. However, such approach uses zero knowledge of natural peptide-receptor interactions which already exists in libraries with 20AA space. There is currently no consensus whether ‘zero knowledge’ naïve 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 peptide from "non-zero knowledge" libraries. We approach this problem by late-stage chemical reshaping of phage-displayed landscape of 20AA binders to NS3aH1 protease. The re-shaping is performed under a novel multifunctional C2-symmetric linchpin, 3,5-bis(bromomethyl)benzaldehyde (termed KYL), that combines two electrophiles that react with thiols and aldehyde group that reacts with N-terminal amine. KYL diversified phage-displayed peptides into bicyclic architectures and delineates 2 distinct sequence populations: (i) peptides that retained binding upon bicyclization (ii) peptides that lost binding once chemically modified. Our report provides a case study for discovering advanced, chemically-upgraded macrocycles and bicycles from libraries with pre-existing knowledge. The results imply that thousands of selection campaigns completed in 20AA space, in principle, can serve for late-stage reshaping and as a starting point for discovery of advanced peptide-derived ligands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".