Nanoflow Gradient Generator Coupled with μ-LC−ESI-MS/MS for Protein Identification
Bibliographic record
Abstract
The large-scale identification of proteins from proteomes of complex organisms, and the availability of various types of protein and DNA databases, increasingly require the additional information provided by tandem mass spectrometry. HPLC and microLC coupled to ESI-MS/MS presently dominate the field of protein identification by tandem mass spectrometry and database searching. The analysis of protein digests is typically performed using HPLC or LC columns with 50-100-microm diameters, requiring the delivery of solvent gradients at low to mid nanoliter per minute flow rates. This has been typically achieved using expensive generic HPLC pumping systems for the delivery of microliter per minute gradients that were either flow-split or sampled. Here we present an alternative system for the delivery of nanoliter per minute gradients. The inexpensive nanoflow gradient generator (etagrad) described here can be modulated to reproducibly deliver selected gradients. The performance of the etagrad on-line with a microLC-ESI-MS/MS system has been demonstrated for the identification of standard protein digests. Moreover, the performance of the etagrad-microLC-ESI-MS/MS system, with protein prefractionation by IPG isoelectric focusing, was also evaluated for rapid study of yeast and human proteomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.004 |
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".