Matrix‐assisted laser desorption/ionization directed nano‐electrospray ionization tandem mass spectrometric analysis for protein identification
Bibliographic record
Abstract
In those cases where the information obtained by peptide mass fingerprinting or matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) is not sufficient for unambiguous protein identification, nano-electrospray ionization (nano-ESI) and/or electrospray ionization tandem mass spectrometry (ESI-MS/MS) analysis must be performed. The sensitivity of nano-ESI/MS, however, is lower than that of MALDI-MS, especially at very low analyte concentrations and/or in the presence of contaminants, such as salt and detergents. Moreover, to perform ESI-MS/MS, the peptide masses of the precursor ions must be known. The approach described in this paper, MALDI-directed nano-ESI-MS/MS, makes use of information obtained from the more sensitive MALDI-MS experiments in order to direct subsequent nano-ESI-MS/MS experiments. Peptide molecular ions found in the MALDI-MS analysis are then selected, as their (+2) precursor ions, for nano-ESI-MS/MS sequencing, even though these ions cannot be detected in the ESI-MS spectra. This method, originally proposed by Tempst et al. (Anal. Chem. 2000, 72: 777-790), has been extended to provide better sensitivity and shorter analysis times; also, a comparison with liquid chromatography/tandem mass spectrometry (LC/MS/MS) has been performed. These experiments, performed using quadrupole time-of-flight instruments equipped with commercially available nano-ESI sources, have allowed the unambiguous identification of in-gel digested proteins at levels below their ESI-MS detection limits, even in the presence of salts and detergents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".