Article Commentary: How Many Proteins are Missed in Quantitative Proteomics Based on MS/MS Sequencing Methods?
Bibliographic record
Abstract
Current bottom-up quantitative proteomics methods based on MS/MS sequencing of peptides are shown to be strongly dependent on sample preparation. Using cytosolic proteins from MCF-7 breast cancer cells, it is shown that protein pre-fractionation based on pI and MW is more effective than pre-fractionation using only MW in increasing the number of observed proteins (947 vs. 704 proteins) and the number of spectral counts per protein. Combination of MS data from the different pre-fractionation methods results in further improvements (1238 proteins). We discuss that at present the main limitation on quantitation by MS/MS sequencing is not MS sensitivity and protein abundance, but rather extensive peptide overlap and limited MS/MS sequencing throughput, and that this favors internally calibrated methods such as SILAC, ICAT or ITRAQ over spectral counting methods in attempts to drastically improve proteome coverage of biological samples.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.043 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".