{"id":"W3189282363","doi":"10.1016/j.saa.2021.120261","title":"Molecular insights into binding mechanism of rutin to bovine serum albumin – Levothyroxine complex: Spectroscopic and molecular docking approaches","year":2021,"lang":"en","type":"article","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","topic":"Protein Interaction Studies and Fluorescence Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare; Ministerul Cercetării şi Inovării; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ontario Ministry of Research, Innovation and Science","keywords":"Rutin; Bovine serum albumin; Chemistry; Antioxidant; Quenching (fluorescence); Denaturation (fissile materials); Binding site; Biophysics; Biochemistry; Fluorescence; Nuclear chemistry; Biology","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":[0.0001602562,0.0004296323,0.000344901,0.0001982394,0.0002595437,0.0002941352,0.0008422795,0.000469156,0.001644082],"category_scores_gemma":[0.0001712718,0.0001671839,0.0003579968,0.0001728929,0.0002679116,0.0005192308,0.0001595583,0.0007598385,0.0002633787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000447094,"about_ca_system_score_gemma":0.0001948828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751067,"about_ca_topic_score_gemma":0.002008006,"domain_scores_codex":[0.9999238,0.00001790345,0.000002497875,0.00001458791,0.0000204318,0.00002074299],"domain_scores_gemma":[0.9999509,0.00001894744,0.00001094729,0.000004055775,0.000007709689,0.000007386528],"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":[0.0002851348,0.0001108561,0.0005139858,0.0002407397,0.00004576699,0.000356501,0.00009229223,0.006697442,0.9804382,0.00338604,0.0003171894,0.007515703],"study_design_scores_gemma":[0.00005055496,0.0002683358,0.003627155,0.00002992481,0.00009664788,0.0006626027,0.0002558162,0.177396,0.8113801,0.001838147,0.004333028,0.00006167298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388407,0.004891409,0.05051675,0.00123008,0.00006074399,0.00003877899,0.0001888444,0.0002987541,0.003933778],"genre_scores_gemma":[0.9892309,0.00157068,0.007650448,0.00007434008,0.000004306692,0.00001405009,0.0001038433,0.00001137892,0.001339979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001751067,"threshold_uncertainty_score":0.005499959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417300016391176,"score_gpt":0.2591121398705415,"score_spread":0.2449391397066298,"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."}}