On-chip Enrichment and Analysis of Peptide Subsets Using a Maleimide-functionalized Fluorous Affinity Biochip and Nanostructure Initiator Mass Spectrometry
Bibliographic record
Abstract
A new nanostructure initiator mass spectrometry (NIMS) methodology is presented that uses the strategy of fluorous-phase immobilization and capture by a maleimide-functionalized affinity tag to selectively enrich peptide subsets containing cysteine residues. This surface-based approach allows complex protein digests to be analyzed. The proposed platform makes use of a chemically unmodified porous silicon (pSi)-based NIMS chip. Unlike matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS), the approach described in this paper does not require analytes to be incorporated or cocrystallized with an initiator. The mass spectra generated by the approach in this work are characterized by low background noise and, therefore, high analyte detection sensitivity. Experiments were also conducted that show the potential the approach described in this work has for generating simplified mass spectra for MS/MS analyses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".