Mapping protein interactions by combining antibody affinity maturation and mass spectrometry
Bibliographic record
Abstract
Mapping protein interactions by immunoprecipitation is limited by the availability of antibodies recognizing available native epitopes within protein complexes with sufficient affinity. Here we demonstrate a scalable approach for generation of such antibodies using phage display and affinity maturation. We combined antibody variable heavy (V(H)) genes from target-specific clones (recognizing Src homology 2 (SH2) domains of LYN, VAV1, NCK1, ZAP70, PTPN11, CRK, LCK, and SHC1) with a repertoire of 10(8) to 10(9) new variable light (V(L)) genes. Improved binders were isolated by stringent selections from these new "chain-shuffled" libraries. We also developed a predictive 96-well immunocapture screen and found that only 12% of antibodies had sufficient affinity/epitope availability to capture endogenous target from lysates. Using antibodies of different affinities to the same epitope, we show that affinity improvement was a key determinant for success and identified a clear affinity threshold value (60 nM for SHC1) that must be breached for success in immunoprecipitation. By combining affinity capture using matured antibodies to SHC1 with mass spectrometry, we identified seven known binding partners and two known SHC1 phosphorylation sites in epidermal growth factor (EGF)-stimulated human breast cancer epithelial cells. These results demonstrate that antibodies capable of immunoprecipitation can be generated by chain shuffling, providing a scalable approach to mapping protein-protein interaction networks.
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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) | 0.001 | 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".