{"id":"W2740231298","doi":"10.1016/j.nutres.2017.07.009","title":"In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I–converting enzyme inhibitory activity","year":2017,"lang":"en","type":"article","venue":"Nutrition Research","topic":"Protein Hydrolysis and Bioactive Peptides","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Infection and Immunity; Ministry of Education, Ethiopia; Izglītības un zinātnes ministrija","keywords":"Tripeptide; In silico; Chemistry; Enzyme; Angiotensin-converting enzyme; Inhibitory postsynaptic potential; Renin–angiotensin system; Pharmacology; Biochemistry; Biology; Peptide; Endocrinology; Blood pressure","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388619,0.000901272,0.0008144846,0.0005609778,0.0003144688,0.0009002356,0.0004548452,0.0005165464,0.003316963],"category_scores_gemma":[0.000686845,0.0002627551,0.001298633,0.0003210791,0.0001869249,0.0002128488,0.0002861499,0.0004334777,0.0007170652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004218032,"about_ca_system_score_gemma":0.0006379298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130829,"about_ca_topic_score_gemma":0.001632257,"domain_scores_codex":[0.9998161,0.00004986316,0.0000108579,0.00004480836,0.00004270676,0.00003567936],"domain_scores_gemma":[0.9996966,0.0001871591,0.00004850952,0.00001057034,0.00002864716,0.00002856622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01175724,0.001501084,0.1320661,0.002371425,0.002397221,0.006285822,0.0001938022,0.3112979,0.4673521,0.005249469,0.004204546,0.05532335],"study_design_scores_gemma":[0.0009005832,0.001472589,0.02601392,0.0001191031,0.002677156,0.001733769,0.0002310914,0.8240827,0.1337432,0.002084648,0.006879986,0.00006127988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685282,0.001459739,0.02253084,0.0002863929,0.00007826596,0.0001009851,0.002623437,0.000477997,0.00391411],"genre_scores_gemma":[0.9831699,0.0003666974,0.01209627,0.0001048624,0.00001745599,0.00004784773,0.003420142,0.00004259084,0.0007342907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003316963,"threshold_uncertainty_score":0.01109636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05087178021763439,"score_gpt":0.3496797911509413,"score_spread":0.2988080109333069,"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."}}