Head‐to‐tail cyclization of side chain‐protected linear peptides to recapitulate genetically‐encoded cyclized peptides
Bibliographic record
Abstract
screens for inhibitors of any target protein of interest. In particular, the Split Intein Circular Ligation of Protein and Peptides (SICLOPPS) system exploits spontaneous protein splicing of inteins to produce intracellular cyclic peptides. A previous SICLOPPS screen against Aurora B kinase, which plays a critical role during chromosome segregation, identified several candidate inhibitors that we sought to recapitulate by chemical synthesis. We describe the syntheses of cyclic peptide hits and analogs via solution-phase macrocyclization of side chain-protected linear peptides obtained from standard solid-phase peptide synthesis. Cyclic peptide targets, including cyclo-[CTWAR], were designed to match both the variable portions and conserved cysteine residue of their genetically-encoded counterparts. Synthetic products were characterized by tandem high-resolution mass spectrometry to analyze a combination of exact mass, isotopic pattern, and collisional dissociation-induced fragmentation pattern. The latter analyses facilitated the distinction between targets and oligomeric side products, and served to confirm peptidic sequences in a manner that can be readily extended to analyses of complex biological samples. This alternative chemical synthesis approach for cyclic peptides allows cost-effective validation and facile chemical elaboration of hit candidates from SICLOPPS screens.
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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 | 0.000 |
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".