Adding polyvinylpyrrolidone to low level protein samples significantly improves peptide recovery in FASP digests: An inexpensive and simple modification to the FASP protocol
Bibliographic record
Abstract
Filter-aided sample preparation (FASP) remains a popular choice for proteomic sample preparation, particularly for its ability to produce a 'clean' peptide sample clear of large molecule contaminants. However, sample loss continues to be a problem particularly for sample inputs that contain less than ten micrograms of protein. Here, we describe that the simple addition of a polymer, polyvinylpyrrolidone-40 (PVP-40) to the protein sample prior to FASP digest significantly improves peptide recovery and identifications, especially with lower level sample inputs. PVP-FASP produces clean samples which required no additional sample clean-up prior to nanoLC-MS analysis. In addition, PVP-FASP is compatible with other FASP modifications, including the use of sodium deoxycholate (DOC) to improve trypsin digestion. SIGNIFICANCE: Simple modification to FASP procedure improves sample recovery during proteomic digests in SDS, improving peptide identifications and median peptide intensity.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".