Detergent-Assisted Glycoprotein Capture: A Versatile Tool for In-Depth N-Glycoproteome Analysis
Bibliographic record
Abstract
Large-scale N-glycoproteome studies have been hindered by poor solubility of hydrophobic membrane proteins and the complexity of proteome samples. Herein, we developed a detergent-assisted glycoprotein capture method to reduce these issues by conducting hydrazide chemistry-based glycoprotein capture in the presence of strong detergents such as sodium dodecyl sulfate and Triton X-100. The strong detergents helped to solubilize hydrophobic membrane proteins and then increased the access of hydrazide groups to oxidized glycoproteins, thus increasing the coverage of the N-glycoproteome. Compared with the conventional glycopeptide capture method, the detergent-assisted glycoprotein capture approach nearly doubled the number of N-glycosylation sites identified from HEK 293T cells with improved specificity. Application of this approach in the larger scale N-glycoproteomics analysis of the HEK 293T cell membrane led to the identification of 2253 unique N-glycosites from 953 proteins. Furthermore, the application of this approach to human serum resulted in the identification of 850 N-glycosylation sites without any immunodepletion or fractionation. Overall, the detergent-assisted glycoprotein capture method simplified the capture process, and it increased the number of sites observed on both hydrophobic membrane proteins and hydrophilic secreted proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".