Membrane proteomics by high performance liquid chromatography–tandem mass spectrometry: Analytical approaches and challenges
Bibliographic record
Abstract
Membrane proteins (MPs) play diverse biologically important structural and functional roles including molecular transport, cell communication, and signal transduction. The dysfunctions of many are linked to deleterious human diseases and thus are of utmost importance in drug discovery. MPs comprise approximately 20-30% of all open reading frames (ORFs), however they are typically under-represented in many LC-MS proteomics experiments due to their low abundance and poor solubility. To address these analytical challenges, various MP enrichment, solubilization, digestion, and fractionation strategies have been employed to further improve the coverage of the membrane systems while maintaining compatibility with MS detection. This review discusses both established and emerging high-throughput gel-free analytical workflows in membrane proteomics, and the inherent advantages, disadvantages, and orthogonality of the various approaches. The issues of critical importance for successful LC-MS/MS detection such as detergent selection and minimizing ion suppression in detergent-based workflows are discussed in detail. Recent studies comparing the performance of different analytical strategies are highlighted in order to provide practical insight into the choice of the most appropriate method for membrane-centric applications ranging from cell surface biomarker discovery to MP interaction network mapping.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".