Monitoring of native chemical ligation on solid substrate by surface plasmon resonance
Bibliographic record
Abstract
Abstract During the last years native chemical ligation (NCL) gained in popularity as a method allowing the chemical synthesis of large peptides and entire proteins. NCL is particularly well‐suited for chemoselective and nondenaturing attachment of biomolecules on solid substrates. In the present work, we show the feasibility of monitoring of peptide synthesis, NCL and its catalysis on silicon oxide modified gold surfaces by surface plasmon resonance (SPR). NCL of a model peptide—bradykinin thioester—was carried out and monitored with a custom‐built SPR apparatus. Solid‐phase produced bradykinin thioester was ligated to the surface in the presence of variable concentrations of 4‐mercaptophenylacetic acid as transthioesterification catalyst. At catalyst concentration of 48 mM and above, the NCL reaction was maximal and identical to the reaction of the purified peptide‐mercaptophenylacetic acid thioester. SPR curves indicate typical first‐order kinetics with t1/2 of 81 s for this aryl thioester, but of 104 min for the primary alkyl thioester. © 2008 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 90: 415–420, 2008. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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 | 0.001 |
| 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".