{"id":"W2963186993","doi":"10.1074/jbc.ra119.009861","title":"Substrate specificity, regiospecificity, and processivity in glycoside hydrolase family 74","year":2019,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Toronto; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Argonne National Laboratory; Ministry of Technology, Innovation and Citizens' Services; Biological and Environmental Research; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canada Foundation for Innovation; U.S. Department of Energy","keywords":"Processivity; Substrate specificity; Glycoside hydrolase; Substrate (aquarium); Hydrolase; Chemistry; Biochemistry; Stereochemistry; Glycoside; Enzyme; Biology; Ecology","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":[0.0002516729,0.0002594109,0.000186606,0.000366952,0.0001596534,0.0003589562,0.0001838112,0.0001844183,0.0005830089],"category_scores_gemma":[0.0003790908,0.00009162995,0.0002860822,0.000386847,0.0002415822,0.0002894301,0.0001630752,0.0002243997,0.0002406849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003575089,"about_ca_system_score_gemma":0.0002929704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751127,"about_ca_topic_score_gemma":0.001468341,"domain_scores_codex":[0.9998631,0.00002112071,0.000006946165,0.00003185105,0.00004577388,0.00003118978],"domain_scores_gemma":[0.999899,0.0000270886,0.00002603342,0.00001192733,0.00002080982,0.00001513687],"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":[0.0002071212,0.00002349683,0.0173181,0.00006968957,0.00002760267,0.0001910127,0.000129565,0.001541849,0.9664779,0.0005542129,0.00007678459,0.01338253],"study_design_scores_gemma":[0.00001275194,0.0003875082,0.1821166,0.00001398119,0.0001085464,0.00180261,0.0004720656,0.01855543,0.7880242,0.001202517,0.007266316,0.00003740026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961305,0.0004772016,0.002499632,0.00002551647,0.000002986455,0.000006473748,0.0001337448,0.00003129603,0.0006926081],"genre_scores_gemma":[0.9964199,0.0001994786,0.002492866,0.000008321107,0.000002659941,0.000003758702,0.0003788652,0.000009203505,0.000485021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001751127,"threshold_uncertainty_score":0.003481865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726399530647456,"score_gpt":0.2369726830808716,"score_spread":0.219708687774397,"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."}}