Profiling Solid-Phase Synthesis Products by Free-Solution Conjugate Capillary Electrophoresis
Bibliographic record
Abstract
Solid-phase synthesis of oligomers, both natural and nonnatural, has proved to be invaluable for the development of many areas of biotechnology. A critical step in the solid-phase synthesis of any oligomer is determining the number and concentration of different constituents present in the product mixture resulting from the synthesis, both before and after purification. Most typically, this analysis is performed by reversed-phase high performance liquid chromatography (RP-HPLC), with the separated components detected by UV absorbance. Recently, we described a novel technique, free-solution conjugate electrophoresis (FSCE), for the high-resolution separation and sensitive laser-induced fluorescence (LIF) detection of uncharged, synthetic polymers, PEG in particular. In this report, we apply this bioconjugate capillary electrophoresis technique to analyze products of the solid-phase synthesis of oligomeric polyamides, namely poly(N-substituted glycines), or polypeptoids. When compared to more traditional RP-HPLC analysis, FSCE analysis of oligomeric peptoids results in separation resolutions that are approximately five times higher and separation efficiencies that are increased by 150%. Moreover, when FSCE with LIF detection is applied to the analysis of oligomeric polyamides after HPLC purification, impurities that are not detectable in RP-HPLC analysis are readily separated and detected. With the advent of capillary array electrophoresis (CAE), which allows for automated, parallel analysis of many different samples, we believe that FSCE will be especially applicable to the analysis of combinatorial synthesis products, by allowing researchers to evaluate many different samples in a single, highly parallel, fully automated analysis. This is in contrast to RP-HPLC analysis, in which samples must be analyzed in series.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".