High-Efficiency Enrichment by Saturating Nanoliters of Protein Affinity Media
Bibliographic record
Abstract
Affinity-purification mass spectrometry (AP-MS) is an established technique for identifying protein-protein interactions (PPIs). The basic technology involves immobilizing a high-specificity ligand to a solid-phase support (e.g., an agarose or magnetic bead) to pull down protein(s) of interest from cell lysates. Although these supports are engineered to minimize interactions with background protein, the conventional method recovers mostly nonspecific binders. The law of mass action for dilute solutions has taught us to use an excess of beads to capture all target proteins, especially weakly interacting ones. However, modern microbead technology presents a binding environment that is much different from a dilute solution. We describe a fluidic platform that captures and processes ultralow nanoliter quantities of magnetic particles, simultaneously increasing the efficiency of PPI detection and strongly suppressing nonspecific binding. We demonstrate the concept with synthetic mixtures of tagged protein and illustrate performance with a variety of AP-MS experiment types. These include a BioID experiment targeting lamin-A interactors from HeLa cells and pulldowns using GFP-tagged proteins associated with a double-strand DNA repair mechanism. We show that efficient extraction requires saturation of the solid-phase support and that <10 nL of beads is sufficient to generate comprehensive protein interaction maps.
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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".