Protocol for dissecting the aggregation-prone protein interactome with optogenetic-induced aggregation and biotin labeling proximity assay
Bibliographic record
Abstract
The dynamics of the early steps of protein aggregation remain poorly understood, particularly in the case of α-synuclein (α-syn) aggregation, the hallmark of synucleinopathies. Here, we present a protocol that combines light-inducible protein aggregation (LIPA) with proximity biotinylation using an UltraID construct. We describe the workflow from protein expression to biochemical validation, including the purification of biotinylated proteins prior to liquid chromatography-mass spectrometry (LC-MS) analysis and subsequent validation. This platform provides a powerful strategy to identify proteins interacting with nascent α-syn aggregates. For complete details on the use and execution of this protocol, please refer to Teixeira et al. 1 • Generation of stable cell line expressing the UltraID-LIPA platform • Efficient screening of interacting proteins with the bait protein of interest • Validation pipeline to identify interacting proteins in cellulo Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The dynamics of the early steps of protein aggregation remain poorly understood, particularly in the case of α-synuclein (α-syn) aggregation, the hallmark of synucleinopathies. Here, we present a protocol that combines light-inducible protein aggregation (LIPA) with proximity biotinylation using an UltraID construct. We describe the workflow from protein expression to biochemical validation, including the purification of biotinylated proteins prior to liquid chromatography-mass spectrometry (LC-MS) analysis and subsequent validation. This platform provides a powerful strategy to identify proteins interacting with nascent α-syn aggregates.
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 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 | 0.001 | 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".