Organic Solvent-Based Protein Precipitation for Robust Proteome Purification Ahead of Mass Spectrometry
Bibliographic record
Abstract
While multiple advances in mass spectrometry (MS) instruments have improved qualitative and quantitative proteome analysis, more reliable front-end approaches to isolate, enrich, and process proteins ahead of MS are critical for successful proteome characterization. Low, inconsistent protein recovery and residual impurities such as surfactants are detrimental to MS analysis. Protein precipitation is often considered unreliable, time-consuming, and technically challenging to perform compared to other sample preparation strategies. These concerns are overcome by employing optimal protein precipitation protocols. For acetone precipitation, the combination of specific salts, temperature control, solvent composition, and precipitation time is critical, while the efficiency of chloroform/methanol/water precipitation depends on proper pipetting and vial manipulation. Alternatively, these precipitation protocols are streamlined and semi-automated within a disposable spin cartridge. The expected outcomes of solvent-based protein precipitation in the conventional format and using a disposable, two-stage filtration and extraction cartridge are illustrated in this work. This includes the detailed characterization of proteomic mixtures by bottom-up LC-MS/MS analysis. The superior performance of SDS-based workflows is also demonstrated relative to non-contaminated protein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".