Membrane proteomics: ‘sweet dreams’ reviving
Bibliographic record
Abstract
Introduction Despite several shortcomings, membrane proteins continue to represent 2/3 rd of new biopharmaceuticals’ targets. It is likely that further improvements in the global expression and distribution of membrane proteins will expedite the development of future drug targets. In our present study we have re‐evaluated the possibility of exploring lectin affinity as an alternate method to study the distribution of membrane proteins in comparison to membrane isolation method. Methodology Isolated membrane from an erythroleukaemic cell line (K562) was washed with the reagents, known to remove non‐covalently associated proteins from the membrane. The proteins of these ‘stripped’ membranes were separated by SDS‐PAGE, in gel digested with trypsin and analyzed by nano‐LC‐ESI‐MS/MS. In a separate experiment cellular glycoproteins were isolated by lectin affinity chromatography employing Con A and WGA lectins. Isolated glycoproteins were digested in‐solution with trypsin and peptide fragments were analyzed by an off‐line RP‐HPLC and MALDI‐TOF. Protein identification was performed using the GPM algorithm. Results A total 624 proteins were identified from the isolated microsome. Of these proteins, 33% were predicted as transmembrane(TM) proteins. Approximately 10% of these transmembrane proteins were also isolated by lectin approach while 9% of lectin purified TM proteins were not identified in microsomal preparation. Conclusion Membrane proteins isolated from two different procedures seem to be complementary to each other. In future, these two procedures may be performed in tandem to increase the representation of membrane proteins in the preparation.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".