Genetically-Encoded Discovery of Proteolytically Stable Bicyclic Inhibitors of Morphogen NODAL
Bibliographic record
Abstract
In this manuscript, we developed a Two-fold Symmetric Linchpin ( TSL ) that converts readily available phage display peptides libraries made of 20 common amino acids to genetically-encoded libraries of bicyclic peptides displayed on phage. TSL combines an aldehyde-reactive group and two thiol-reactive groups; it bridges two side chains of cysteine [C] with an N-terminal aldehyde group derived from the N-terminal serine [S], yielding a novel bicyclic topology that lacks a free N-terminus. Phage display libraries of SX 1 CX 2 X 3 X 4 X 5 X 6 X 7 C sequences, where X i is any amino acids but Cys, were converted to a library of bicyclic TSL -[ S ]X 1 [C] X 2 X 3 X 4 X 5 X 6 X 7 [ C] peptides in 45 ± 15% yield. Using this library and protein morphogen NODAL as a target, we discovered bicyclic macrocycles that specifically antagonize NODAL-induced signaling in cancer cells. At a 10 µM concentration, two discovered bicyclic peptides completely suppressed NODAL-induced phosphorylation of SMAD2 in P19 embryonic carcinoma. The TSL -[ S ]Y [C] KRAHKN[ C] bicycle inhibited NODAL-induced proliferation of NODAL-Tky-nu ovarian carcinoma cells with apparent IC50 1 µM. The same bicycle at 10 µM concentration did not affect the growth of the control Tky-nu cells. TSL -bicycles remained stable over the course of the 72 hour-long assays in a serum-rich cell-culture medium. We further observed general stability in mouse serum and in a mixture of proteases (Pronase TM ) for 33 diverse bicyclic macrocycles of different ring sizes, amino acid sequences, and cross-linker geometries. TSL -constrained peptides expand the previously reported repertoire of phage display bicyclic architectures formed by cross-linking Cys side chains. We anticipate that it will aid the discovery of proteolytically stable bicyclic inhibitors for a variety of protein targets.
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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".