Molecular mechanisms in clathrin-mediated membrane budding revealed through subcellular proteomics
Bibliographic record
Abstract
Subcellular proteomics is a powerful new approach that combines subcellular fractionation and MS (mass spectrometry) to identify the protein complement of cellular compartments. The approach has been applied to isolated organelles and major suborganellar structures and each study has identified known proteins not previously understood to associate with the compartment and novel proteins that had been described only as predicted open-reading frames from genome sequencing data. We have utilized subcellular proteomics to analyse the protein components of CCVs (clathrin-coated vesicles) isolated from adult brain. Accounting for identified fragmented peptides allows for a quantitative assessment of protein complexes associated with CCVs, and the identification of many of the known components of post-fusion synaptic vesicles demonstrates that a main function for brain CCVs is to recycle synaptic vesicles. In addition, we have identified a number of novel proteins that participate in CCV formation and function at the trans-Golgi network and the plasma membrane. Characterization of two of these proteins, NECAP1 and NECAP2, has led to the identification of a new consensus motif that mediates protein interactions with the clathrin adaptor protein 2. These studies highlight the ability of proteomics to reveal new insights into the mechanisms and functional roles of subcellular compartments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".