Artifacts to avoid while taking advantage of top‐down mass spectrometry based detection of protein S‐thiolation
Bibliographic record
Abstract
Bottom-up MS studies typically employ a reduction and alkylation step that eliminates a class of PTM, S-thiolation. Given that molecular oxygen can mediate S-thiolation from reduced thiols, which are abundant in the reducing intracellular milieu, we investigated the possibility that some S-thiolation modifications are artifacts of protein preparation. Cu/Zn-superoxide dismutase (SOD1) was chosen for this case study as it has a reactive surface cysteine residue, which is readily cysteinylated in vitro. The ability of oxygen to generate S-thiolation artifacts was tested by comparing purification of SOD1 from postmortem human cerebral cortex under aerobic and anaerobic conditions. S-thiolation was ∼50% higher in aerobically processed preparations, consistent with oxygen-dependent artifactual S-thiolation. The ability of endogenous small molecule disulfides (e.g. cystine) to participate in artifactual S-thiolation was tested by blocking reactive protein cysteine residues during anaerobic homogenization. A 50-fold reduction in S-thiolation occurred indicating that the majority of S-thiolation observed aerobically was artifact. Tissue-specific artifacts were explored by comparing brain- and blood-derived protein, with remarkably more artifacts observed in brain-derived SOD1. Given the potential for such artifacts, rules of thumb for sample preparation are provided. This study demonstrates that without taking extraordinary precaution, artifactual S-thiolation of highly reactive, surface-exposed, cysteine residues can result.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".