Target‐Templated Construction of Functional Proteomimetics Using Photo‐Foldamer Libraries
Bibliographic record
Abstract
Abstract Current methods for proteomimetic engineering rely on structure‐based design. Here we describe a design strategy that allows the construction of proteomimetics against challenging targets without a priori characterization of the target surface. Our approach employs (i) a 100‐membered photoreactive foldamer library, the members of which act as local surface mimetics, and (ii) the subsequent affinity maturation of the primary hits using systems chemistry. Two surface‐oriented proteinogenic side chains drove the interactions between the short helical foldamer fragments and the proteins. Diazirine‐based photo‐crosslinking was applied to sensitively detect and localize binding even to shallow and dynamic patches on representatively difficult targets. Photo‐foldamers identified functionally relevant protein interfaces, allosteric and previously unexplored targetable regions on the surface of STAT3 and an oncogenic K‐Ras variant. Target‐templated dynamic linking of foldamer hits resulted in two orders of magnitude affinity improvement in a single step. The dimeric K‐Ras ligand mimicked protein‐like catalytic functions. The photo‐foldamer approach thus enables the highly efficient mapping of protein‐protein interaction sites and provides a viable starting point for proteomimetic ligand development without a priori structural hypotheses.
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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) | 0.001 | 0.001 |
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".