A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
Bibliographic record
Abstract
Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for ‘orphan’ receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".