{"id":"W4413836732","doi":"10.1038/s41467-025-63357-7","title":"Characterization of PROTAC specificity and endogenous protein interactomes using ProtacID","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Genentech; European Federation of Pharmaceutical Industries and Associations; University of Toronto; Ontario Genomics Institute; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; McGill University; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Biotinylation; Computational biology; Chromatin; Interactome; Ubiquitin ligase; Cell biology; Protein degradation; Ubiquitin; Biotin; Biology; Molecular biology; DNA; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003973843,0.000480965,0.0003265195,0.0004054147,0.0004549749,0.0005825213,0.0004164411,0.0004591297,0.001679448],"category_scores_gemma":[0.0003435527,0.0002760147,0.0003398782,0.0002204619,0.0003804704,0.0004540856,0.0005513056,0.001002163,0.001122571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004386406,"about_ca_system_score_gemma":0.0002603862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005819843,"about_ca_topic_score_gemma":0.001641311,"domain_scores_codex":[0.9996123,0.00005400119,0.00002674497,0.0000936439,0.0001640473,0.00004931478],"domain_scores_gemma":[0.9995396,0.0001243244,0.00009676631,0.00007493104,0.0000887694,0.00007566898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003932044,0.00000589574,0.000101169,0.00003148528,0.000003976506,0.00002815926,0.00001509927,0.00003819862,0.9986499,0.00009485443,0.00002860241,0.0009634615],"study_design_scores_gemma":[0.000003999839,0.00005100543,0.001190119,0.000004023966,0.000004683811,0.0002969204,0.00001562852,0.0007540276,0.993845,0.00003652909,0.003792387,0.000005610269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7997109,0.004051063,0.1868924,0.0004953213,0.000074764,0.000281813,0.001269448,0.0007879633,0.006436409],"genre_scores_gemma":[0.8385196,0.002724579,0.1370466,0.0004160079,0.00002512214,0.0002699824,0.005708807,0.0004030574,0.01488625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001679448,"threshold_uncertainty_score":0.005618334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236906525140938,"score_gpt":0.2919824224981294,"score_spread":0.26961335724672,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}