PPInteractors

One-sentence summary: I rebuilt the SARS-CoV-2 host interactome from the Krogan lab's raw AP-MS data using a novel updatable confidence score that lets structural and chemical evidence rescue borderline interactions, and found that the strongest biology (centrosome hijack via PCNT) is not druggable while a clinical-translatability filter inverts the compound ranking to surface safer repurposing leads, showing that multi-evidence, clinically-aware scoring turns a raw interaction map into an honest, prioritized set of therapeutic opportunities rather than an overclaimed cure. What I built / investigated I built an end-to-end computational pipeline that turns the Krogan lab's raw SARS-CoV-2 AP-MS data into a clinically-filtered, ranked list of therapeutic opportunities, reconstructing the virus/host interactome from scratch rather than trusting the published hit list. Starting from 22,153 unthresholded bait-prey measurements (27 viral baits × 2,750 human proteins), the pipeline runs through six stages, each handled by a specialist role: data QC, network reconstruction, target biology, structural tractability, compound identification/docking, and a clinical and translational reality check. The methodological centerpiece is an updatable composite confidence score. Instead of a fixed pass/fail cutoff, every interaction carries a continuous posterior probability (log-odds space) that combines the non-redundant MS signals, and crucially lets downstream structural and chemical evidence plug in as extra likelihood terms, so a sub-threshold interaction can be recovered when orthogonal data corroborate it. What I found A better, re-iterable scoring metric. The composite posterior hits ROC-AUC 0.999 / PR-AUC 0.955 vs 0.618 for the best single published metric (MIST). The "updatable" design worked end to end: orf10-ELOB was rescued from the marginal tier into high-confidence on the strength of a solved structure (PDB 9BIE), and external complex-membership evidence rescued 61 marginal pairs into HIGH on grounds fully independent of the AP-MS scoring. The strongest biology is centrosome hijack. Three independent axes (AP-MS confidence, network centrality, and evolutionary positive selection via PAML) converge on PCNT (Pericentrin) as the #1 host target (positive-selection q = 5.4×10⁻²⁷). Several single viral baits each bind all subunits of one human complex (nsp1 to DNA polymerase-α/primase; nsp13/M to the Pericentrin-GCP centrosome; nsp8 to AATF-NGDN-NOL10). The clinical filter inverts the compound ranking. Purely biochemical ranking favors oncology drugs (nucleoside analogs, proteasome inhibitors). Adding a clinical-translatability term collapses those and lifts safer options (COMT inhibitors, an entry-inhibitor probe, fostamatinib). Final verdict on 17 near-term repurposing opportunities: 0 clean GO, 3 CONDITIONAL, 14 NO-GO. Notably, XPO1/selinexor, the only candidate ever tested in a randomized COVID-19 trial, was discontinued for futility and trended toward harm, exactly matching our NO-GO call. Why it matters The project delivers three honest categories of output rather than an overclaimed "cure": (1) near-term repurposing leads that are hypothesis-generating and need dedicated early-phase trials; (2) a differentiated novel-modality target, the CRL2^ZYG11B/orf10 viral neo-interaction, as the one axis with a virus-selective rationale; and (3) fundamental virology (the centrosome-hijack axis) that is publishable science but not a drug target due to essential-gene toxicity. Methodologically, it shows that multi-evidence, updatable scoring beats any single AP-MS metric, the right response to the field's well-documented low cross-lab reproducibility, and that baking a clinical-translatability term into target prioritization prevents pursuing biochemically-attractive but clinically-implausible candidates. Finally, the project does not stop at analysis: it delivers a complete, Cell-format manuscript, ready for internal review ahead of submission, with a full figure set (Figures 1 to 6 plus supplements), STAR Methods, and all underlying data tables, packaging the entire pipeline and its findings into a publication-ready document.