{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000272326,0.0001254283,0.0002015771,0.00002255882,0.00002089033,0.00002554704,0.0001412028,0.0002068126,0.00004916577],"category_scores_gemma":[0.0000860185,0.00009694792,0.00006331246,0.00007821465,0.00007148201,0.00001011617,0.00006457006,0.0002229098,0.000004193829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001648378,"about_ca_system_score_gemma":0.00003901515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001064465,"about_ca_topic_score_gemma":8.049767e-7,"domain_scores_codex":[0.9991274,0.00003561148,0.0003398908,0.0002407887,0.0001079119,0.0001483586],"domain_scores_gemma":[0.999417,0.00001075979,0.0002602467,0.000136113,0.00009356483,0.00008231258],"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":[0.0002085287,0.0000901058,0.07493331,0.00003065785,0.00000921783,0.00001109692,0.000008870552,0.00001277545,0.9243196,0.000005313089,0.0001348187,0.0002357115],"study_design_scores_gemma":[0.0005868802,0.0001878622,0.09330111,0.00002974808,0.000004056384,0.0001754607,0.000103875,0.00001303821,0.9007904,0.0001276896,0.004537493,0.0001424001],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977918,0.0005805263,0.00005261599,0.0002325274,0.00008749132,0.00006281972,0.000004200333,0.00000460982,0.001183415],"genre_scores_gemma":[0.9982402,0.0008620115,0.0001017361,0.0001260186,0.000327112,0.000001297431,0.00002016461,0.000007138803,0.0003143625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02352921,"threshold_uncertainty_score":0.3953421,"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."}}