Nanoliter Sample Handling Combined with Microspot MALDI−MS for Detection of Gel-Separated Phosphoproteins
Bibliographic record
Abstract
We describe a microspot matrix-assisted laser desorption ionization (MALDI) mass spectrometric approach to analyze gel-separated phosphoproteins. This method involves in-gel digestion of phosphoproteins after gel separation, followed by open tubular capillary (OTC) immobilized metal-ion affinity chromatography (IMAC) to capture the phosphopeptides with markedly reduced interferences from nonphosphorylated peptides. Nanoliter-volume of ammonium phosphate is used to elute the phosphopeptides captured on the capillary tube. After mixing with a small volume of matrix solution in the capillary, the effluent is deposited in a microspot on a sample plate for MALDI-MS analysis. It is also shown that, with peptide esterification after in-gel digestion of a phosphoprotein, negative ion detection in MALDI gives a distinct advantage over the positive ion mode of operation for phosphopeptide analysis, even without IMAC enrichment. However, the OTC-IMAC technique is demonstrated to be superior to the approach of negative ion detection of esterified in-gel digests without IMAC. OTC-IMAC is found to be sufficiently selective to capture phosphopeptides from in-gel digest of a gel band containing predominately one protein and the combination of peptide esterification and IMAC enrichment does not provide any real advantage. Using a standard phosphoprotein alpha-casein as a model system, we demonstrate that this OTC-IMAC method can detect a number of phosphopeptides after in-gel digestion with mid-fmol protein sample loading. An example of real world applications of this method is illustrated in the characterization of a fusion protein, His182, expressed in E. coli.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".