Extending the Compatibility of the SP3 Paramagnetic Bead Processing Approach for Proteomics
Bibliographic record
Abstract
The diversity in protein and peptide biochemistry necessitates robust protocols and reagents for efficiently handling and enriching these molecules prior to analysis with mass spectrometry (MS) or other techniques. Further exploration of the paramagnetic bead-based approach, single-pot solid-phase-enhanced sample preparation (SP3), is carried out toward updating and extending previously described conditions and experimental workflows. The SP3 approach was tested in a wide range of experimental scenarios, including (1) binding solvents (acetonitrile, ethanol, isopropanol, acetone), (2) binding pH (acidic vs neutral), (3) solvent/lysate ratios (50-200%, v/v), (4) mixing and rinsing conditions (on-rack vs off-rack rinsing), (5) Enrichment of nondenatured proteins, and (6) capture of individual proteins from noncomplex mixtures. These results highlight the robust handling of proteins in a broad set of scenarios while also enabling the development of a modified SP3 workflow that offers extended compatibility. The modified SP3 approach is used in quantitative in-depth proteome analyses to compare it with commercial paramagnetic bead-based HILIC methods (MagReSyn) and across multiple binding conditions (e.g., pH and solvent during binding). Together, these data reveal the extensive quantitative coverage of the proteome possible with SP3 independent of the binding approach utilized. The results further establish the utility of SP3 for the unbiased handling of peptides and proteins for proteomic applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".