Receptor Interactome Discovery with RDIMIS, a Membrane Protein Interaction Screen Using Recombinant Extracellular Vesicles
Bibliographic record
Abstract
Membrane protein interactions are challenging to identify because of the unique biophysical characteristics of both transmembrane proteins and membrane environments. The Receptor Display in Membranes Interaction Screen (RDIMIS) platform overcomes these challenges by screening transmembrane and membrane-proximal proteins in a membrane environment using recombinant extracellular vesicles (rEVs). The screen has been used to successfully identify interactions for difficult-to-study receptors in an unbiased manner. In this report, we detail how we generate rEVs, characterize the rEVs to ensure screen-readiness, and perform the full interaction screening, with emphasis on the criteria necessary to obtain clear, interpretable results. We also include support protocols for generating a screening library and validating screening results, as well as an alternate protocol for RDIMIS enabling the profiling of naturally occurring extracellular vesicles. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Generating and isolating extracellular vesicles from cells Basic Protocol 2: Characterizing recombinant extracellular vesicles Support Protocol 1: Preparing the receptor screening library Basic Protocol 3: Performing the Receptor Display in Membranes Interaction Screen (RDIMIS) Support Protocol 2: Validating RDIMIS results using microscopy Alternate Protocol: Detecting unlabeled endogenous vesicles.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".