Identification and characterization of cysteinyl exposure in proteins by selective mercury labeling and nano‐electrospray ionization quadrupole time‐of‐flight mass spectrometry
Bibliographic record
Abstract
We describe a method for probing surface-exposed cysteines in proteins by selective labeling with p-hydroxymercuribenzoate (PMB) combined with nano-electrospray ionization mass spectrometric analysis (nanoESI-MS). The rapid, stoichiometric, and specific labeling by PMB of surface-exposed cysteines allows for characterization of the accessibility of the cysteines using a single MS analysis. Moreover, by taking advantage of the large mass shift of 321 Da, unique isotopic pattern, and enhanced MS signal of PMB-labeled cysteine-containing peptide fragments, the surface-exposed cysteines in proteins can be accurately identified by peptide mapping. The number and sites of reactive cysteines on the surface of human and rat hemoglobins (hHb and rHb) were identified as examples. Collision-induced dissociation tandem mass spectrometric (MS/MS) analysis of specific peptides further confirmed the selective labeling of PMB in hHb. The subtle difference between the different cysteine residues in rHb was also evaluated by multiple PMB titrations. The difference between the two cysteines in their environment may partially explain their reaction specificity. Cysteine 125 in the beta unit of rHb is exposed on the surface, explaining its reactivity with glutathione. Cysteine 13 in the alpha subunit of rHb is much less exposed, and is located in a hydrophobic pocket, a conclusion that is consistent with the previous observation of its selective binding with dimethylarsinous acid, a reactive arsenic metabolite. The method is potentially useful for probing cysteines in other biologically important proteins and for studying proteins that are associated with conformational or structural changes induced by denaturing processes, protein modifications, protein-protein interactions and protein assemblies.
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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 | 0.000 | 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) | 0.000 | 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 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".