A fast, reproducible and low‐cost method for sequence deconvolution of ‘on‐bead’ peptides via ‘on‐target’ maldi‐TOF/TOF mass spectrometry
Bibliographic record
Abstract
A novel approach to high-throughput sequence deconvolution of on-bead small peptides (MW < 2000 Da) using on-target MALDI-TOF/TOF instrumentation is presented. Short peptides of pentamer and octamer length, covalently attached to TentaGel polystyrene beads through a photolabile linker, were placed onto the MALDI target, apportioned with suitable matrix (2,5-dihydroxybenzoic acid) and then hit with the instrument laser (Nd : YAG, 355 nm). This induced easy and highly reproducible photochemical cleavage, desorption (MS mode) and fragmentation (MS/MS mode). Peptide fragments were identified with a mass accuracy of 0.1 Da of the expected values. This technique significantly accelerates the sequence determination of positive peptide hits obtained from random combinatorial libraries when screening against biological targets, paving the way for a rapid and efficient method to identify molecular imaging ligands specific to pathological targets in cancer and other diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".