Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids
Bibliographic record
Abstract
Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 antivirals. However, how the ancestral virus and its variants remodel virus-host protein assemblies in various organ-specific cells and bodily fluids remains unclear. Here, we conduct 639 affinity-purifications by tagging and expressing 28 SARS-CoV-2 and spike proteins from the ancestral virus and four variants in eight cell lines representing five mammalian organs and the immune system. Using mass spectrometry (MS), we identify both known and previously unreported SARS-CoV-2-human PPIs, highlighting similarities and differences across organ- or immune-derived cell lines and virus strains. Besides verifying the cell- and variant-specific PPIs, co-fractionation-MS analysis of COVID-19 patients' saliva confirm host PPI changes between SARS-CoV-2 strains. We discover that the NSP3 papain-like protease, a secreted protein, binds fibrinogen to induce abnormal blood clotting and interferon-induced proteins to evade host innate immune responses. Leveraging deep learning, we design peptide inhibitors that successfully blocked SARS-CoV-2 and variant replication in human liver cells, reversing virus-induced PPI alterations. Together, these findings provide molecular insights into SARS-CoV-2 biology, uncover reorganized viral-host protein assemblies during infection, and identify potential host therapeutic targets and inhibitors for developing antivirals against SARS-CoV-2 strains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".