Bibliographic record
Abstract
Compartmentalization is essential for all complex forms of life. In eukaryotic cells, membrane-bound organelles, as well as a multitude of protein- and nucleic acid-rich subcellular structures, maintain boundaries and serve as enrichment zones to promote and regulate protein function, including signaling events. Understanding the composition of each cellular “compartment” (be it a classical organelle or a large protein complex) remains a challenging task. For soluble protein complexes, different approaches coupled to mass spectrometry such as affinity purification, biochemical fractionation and proximity labeling of proteins in vivo using biotin-transfer provide important insight. For detergent-insoluble components, proximity labeling has been very effective in identifying protein neighbors, leading to rapid uptake of this technique in the scientific community. This presentation will introduce and compare protein-protein interaction mapping techniques and describe how to design, execute, and analyze proximity labeling experiments, with an emphasis on performing these experiments in a core facility. We have generated a human cell map of major organelles and non-membrane-bound structures from proteins profiled by in vivo biotinylation (BioID). This resource is now a community tool, and I will discuss how it can be used to better interpret BioID results. Since its introduction less than a decade ago, there has been tremendous progress in applying and improving proximity labeling techniques, and I will conclude with a glimpse of what is next on the horizon for proximity mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".