Analyses of PCSK9 Post-translational Modifications Using Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
Post-translational modification(s) can affect a protein's function - changing its half-life/stability, its protein-protein interactions, biological activity and/or sub-cellular localization. Following translation, a protein can be modified in several ways, including (i) disulfide bridge formation, (ii) chemical conversion of its constituent amino acids (for instance, glutamine can undergo deamidation to glutamic acid), (iii) sulfation, phosphorylation, de/acetylation, and glycosylation (to name a few), (iv) addition of other proteins as occurs during sumoylation and ubiquitination, and (v) proteolytic cleavage(s). There are several techniques available to identify and monitor post-translational modifications of proteins and peptides including mass spectrometry, two-dimensional sodium dodecyl sulfate polyacrylamide electrophoresis (2D-SDS-PAGE), radiolabeling, and immunoblotting. Ciphergen's surface-enhanced laser desorption/ionization time-of-flight mass spectrometer (SELDI-TOF-MS) has been used successfully for protein/peptide profiling in disease states and for the detection of protein/peptide biomarkers (1-4). In this chapter, the secreted proprotein convertase subtilisin/kexin 9 (PCSK9), which we study in our lab, is used to demonstrate coupling of immunoprecipitation with Ciphergen's time-of-flight mass spectrometer and its ProteinChip software to detect and analyze the common post-translational modifications of phosphorylation and glycosylation. The following topics are covered (1): preparation of cell extracts/samples/spent media (2), processing of samples by immunoprecipitation including optimization of conditions and (3) data acquisition by mass spectrometry and its subsequent analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".