{"id":"W3202854404","doi":"10.1016/j.bbapap.2021.140720","title":"Binding interactions in a kinase active site modulate background ATP hydrolysis","year":2021,"lang":"en","type":"article","venue":"Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics","topic":"Biochemical and Molecular Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Isothermal titration calorimetry; Chemistry; Enzyme kinetics; ATP hydrolysis; Nucleoside; Kinase; Product inhibition; Substrate (aquarium); Allosteric regulation; Biophysics; Stereochemistry; Hydrolysis; Active site; Nucleoside triphosphate; Pyrophosphate; Biochemistry; Adenosine triphosphate; Enzyme; Titration; Nucleotide; ATPase; Non-competitive inhibition; Biology; Organic chemistry","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":[0.0003700212,0.0004125868,0.0006372465,0.0002726652,0.0007833678,0.001434089,0.0006573814,0.000676506,0.0111002],"category_scores_gemma":[0.0008846115,0.0004930532,0.0002630547,0.0002838419,0.0004241974,0.0009477174,0.0008423104,0.00101554,0.00239137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006409101,"about_ca_system_score_gemma":0.0002820879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008968262,"about_ca_topic_score_gemma":0.001363682,"domain_scores_codex":[0.9994543,0.00006895013,0.00003233804,0.0001488793,0.0001584925,0.0001370866],"domain_scores_gemma":[0.9995276,0.0001711521,0.00006161164,0.00004778929,0.00004870499,0.000143224],"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":[0.0005455358,0.0001136829,0.0006454578,0.00008368133,0.00002311248,0.0001107279,0.00003576019,0.0002752514,0.9952912,0.0005545689,0.0001898111,0.002131188],"study_design_scores_gemma":[0.00008069837,0.0002599449,0.01262987,0.00002533433,0.00005832242,0.0004229012,0.0001281318,0.008264763,0.9734957,0.0008134573,0.003787765,0.00003312132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906053,0.001088172,0.002944465,0.0002793676,0.00009558699,0.00002968804,0.0002367006,0.0001215614,0.0045992],"genre_scores_gemma":[0.9962334,0.0002482531,0.0009298893,0.0001324472,0.00002097305,0.00001271699,0.0002124025,0.00005262071,0.002157336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0111002,"threshold_uncertainty_score":0.03713387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980897897023864,"score_gpt":0.2855405625878715,"score_spread":0.2657315836176328,"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."}}