Characterization of cysteine residues and disulfide bonds in proteins by liquid chromatography/electrospray ionization tandem mass spectrometry
Bibliographic record
Abstract
Cysteine residues and disulfide bonds are important for protein structure and function. We have developed a simple and sensitive method for determining the presence of free cysteine (Cys) residues and disulfide bonded Cys residues in proteins (<100 pmol) by liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) in combination with protein database searching using the program Sequest. Free Cys residues in a protein were labeled with PEO-maleimide biotin immediately followed by denaturation with 8 M urea. Subsequently, the protein was digested with trypsin or chymotrypsin and the resulting products were analyzed by capillary LC/ESI-MS/MS for peptides containing modified Cys and/or disulfide bonded Cys residues. Although the MS method for identifying disulfide bonds has been routinely employed, methods to prevent thiol-disulfide exchange have not been well documented. Our protocol was found to minimize the occurrence of the thiol-disulfide exchange reaction. The method was validated using well-characterized proteins such as aldolase, ovalbumin, and beta-lactoglobulin A. We also applied this method to characterize Cys residues and disulfide bonds of beta 1,4-galactosyltransferase (five Cys), and human blood group A and B glycosyltransferases (four Cys). Our results demonstrate that beta 1,4-galactosyltransferase contains one free Cys residue and two disulfide bonds, which is in contrast to work previously reported using chemical methods for the characterization of free Cys residues, but is consistent with recently published results from x-ray crystallography. In contrast to the results obtained for beta 1,4-galactosyltransferase, none of the Cys residues in A and B glycosyltransferases were found to be involved in disulfide bonds.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".