Ribosomal synthesis of nonstandard peptidesThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Systems and Chemical Biology, and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
It is well known that standard peptides, which comprise proteinogenic amino acids, can act as specific chemical probes to target proteins with high affinity. Despite this fact, a number of peptide drug leads have been abandoned because of their poor cell permeability and protease instability. On the other hand, nonstandard peptides isolated as natural products often exhibit remarkable pharmaco-behavior and stability in vivo. Although it is likely that numerous nonstandard therapeutic peptides capable of recognizing various targets could have been synthesized, enzymes for nonribosomal peptide syntheses are complex; therefore, it is difficult to engineer such modular enzymes to build nonstandard peptide libraries. Here we describe an emerging technology for the synthesis of nonstandard peptides that employs an integrated system of reconstituted cell-free translation and flexizymes. We summarize the historical background of this technology and discuss its current and future applications to the synthesis of nonstandard peptides and drug discovery.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".