Comparative Evaluation of Solid-phase and Membrane Mimetic Strategies in Membrane Proteome Coverage and Disease-State Analysis
Bibliographic record
Abstract
Membrane proteins (MPs) are vital to cellular signaling, metabolism, and disease pathology, yet remain underrepresented in proteomics. To address this, several independent workflows have been developed to enable the profiling of the membrane proteome; however, the relative advantages and limitations of each method remain poorly defined. Here, we systematically compare four classical solid-phase membrane proteomic workflows (SP3, SP4, FASP, S-Trap) and three membrane mimetic strategies (Peptidisc, nanodisc, and SMALP copolymer) for mass spectrometry-based membrane proteome profiling, using healthy (LFD) and obese (HFD) mouse liver tissue. We found that the solid-phase methods yield higher total protein identifications, while the membrane mimetic systems enrich MPs. SMALP copolymer displays intermediate characteristics between the solid-phase and membrane mimetic workflows. Peptidisc and nanodisc stand out for their enrichment of MPs, although Peptidisc shows better enrichment of plasma membrane integral MPs, particularly those with 11+ transmembrane segments. In the context of HFD-induced liver proteome remodeling, the Peptidisc workflow outperformed the other six methods by capturing the highest number of differentially expressed MPs and demonstrating the lowest standard deviation of MP-level dysregulation. Collectively, this comparative analysis highlights the trade-offs between depth of proteome coverage and MP enrichment across workflows, underscoring the importance of method selection based on total protein counts, MP enrichment, and the precise detection of MP-level dysregulation.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".