{"id":"W2768709594","doi":"10.1021/acs.analchem.7b02395","title":"Quantifying Missing (Phospho)Proteome Regions with the Broad-Specificity Protease Subtilisin","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen; Bundesministerium für Bildung und Forschung; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Subtilisin; Proteome; Chemistry; Trypsin; Protease; In silico; Proteolysis; Peptide; Proteolytic enzymes; Biochemistry; Serine protease; Enzyme; Computational biology; Biology; Gene","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001618492,0.0002795847,0.0002624189,0.000009757064,0.001431585,0.0003550269,0.0009778179,0.0001937979,0.0004952899],"category_scores_gemma":[0.0002022524,0.0001972809,0.0001443201,0.00009288677,0.0006614858,0.0001560908,0.0002091465,0.0007027376,0.00001914751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009582674,"about_ca_system_score_gemma":0.00008976889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009483458,"about_ca_topic_score_gemma":0.000009534726,"domain_scores_codex":[0.9984273,0.000006825614,0.0002750779,0.0005638881,0.0002947258,0.0004322197],"domain_scores_gemma":[0.9972641,0.00008250292,0.0003074301,0.002003212,0.0001239842,0.0002188],"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":[0.0003250706,0.0004531353,0.01039745,0.0005804642,0.0001873082,0.0001343319,0.0001235723,0.00004221635,0.9646971,0.01382712,0.004819895,0.004412361],"study_design_scores_gemma":[0.0003758608,0.000009506045,0.0003783469,0.0001797332,0.00008408351,0.0000519672,0.0001462127,0.002517746,0.9608748,0.003855166,0.03106333,0.0004632208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8415212,0.0001701125,0.04011178,0.01715942,0.00001315228,0.0005348222,0.0000625828,0.0004675622,0.09995939],"genre_scores_gemma":[0.9861906,0.00002554771,0.007741447,0.00009079916,0.0002166869,0.00020031,0.0000225868,0.00004636005,0.00546569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1446694,"threshold_uncertainty_score":0.9998684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04711638891304788,"score_gpt":0.3174457207972944,"score_spread":0.2703293318842465,"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."}}