{"id":"W2914019332","doi":"10.1074/mcp.tir118.001099","title":"Quantitative Multiplex Substrate Profiling of Peptidases by Mass Spectrometry","year":2019,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Peptidase Inhibition and Analysis","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of General Medical Sciences; Office of the President, University of California; Ray Thomas Edwards Foundation; University of California, San Diego; National Institutes of Health; American Cancer Society","keywords":"Proteolysis; Chemistry; Peptide; Proteases; Tandem mass spectrometry; Biochemistry; Cleavage (geology); Isobaric labeling; Mass spectrometry; Chromatography; Protein mass spectrometry; Enzyme; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496258,0.000243128,0.0005036184,0.0002356117,0.0000382672,0.000024376,0.0001193355,0.0001351895,0.0003303966],"category_scores_gemma":[0.00009061306,0.0002347037,0.0003312092,0.0004676666,0.00009667566,0.00007612416,0.00003453628,0.0002804113,0.0001652371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006571144,"about_ca_system_score_gemma":0.00009335157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002980784,"about_ca_topic_score_gemma":8.8383e-7,"domain_scores_codex":[0.998306,0.00008823608,0.0004546215,0.0004185663,0.0004099236,0.0003226829],"domain_scores_gemma":[0.9989532,0.00003015896,0.0002416154,0.0004445171,0.0001845076,0.0001460302],"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":[0.0001470404,0.0001864134,0.004145479,0.0002395982,0.0001617928,0.00009363229,0.00005253625,0.00007942235,0.9939573,0.0008786758,0.00003945549,0.00001869647],"study_design_scores_gemma":[0.001439791,0.0003565778,0.0001606455,0.0002520281,0.0001012516,0.00001060453,0.0003038415,0.005708927,0.9912044,0.0001939541,0.00003525441,0.00023268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9306054,0.0002872039,0.06627874,0.0001466099,0.00001928992,0.0009378566,0.00005647502,0.00005344214,0.001614994],"genre_scores_gemma":[0.9249159,0.00001312471,0.07384536,0.0001526709,0.00001897663,0.00002659685,0.0002972834,0.00004672773,0.0006834127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007566625,"threshold_uncertainty_score":0.9570939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045073799487136,"score_gpt":0.248440548239098,"score_spread":0.2379898102442266,"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."}}