{"id":"W2944203220","doi":"10.1016/j.chembiol.2019.03.017","title":"Development and Application of a High-Throughput Functional Metagenomic Screen for Glycoside Phosphorylases","year":2019,"lang":"en","type":"article","venue":"Cell chemical biology","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Genome British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phosphorolysis; Glycoside hydrolase; Glycosidic bond; Glycosyltransferase; Metagenomics; Cellobiose; High-throughput screening; Glycogen phosphorylase; Biochemistry; Glycoside; Biology; Microbiome; Enzyme; Chemistry; Computational biology; Cellulase; Gene; Bioinformatics; Purine nucleoside phosphorylase; Botany","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":[0.00005940708,0.00008007688,0.0001129517,0.00001609298,0.00001992319,0.000002723034,0.00005279273,0.0001184468,0.00002555122],"category_scores_gemma":[0.00001586509,0.00007539184,0.00002893325,0.0000277564,0.00005113427,0.000002141341,0.00004925911,0.00002829652,0.00001020653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008541171,"about_ca_system_score_gemma":0.00002876077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005441974,"about_ca_topic_score_gemma":0.000001470231,"domain_scores_codex":[0.9994243,0.000009196177,0.0001585964,0.0002864282,0.00002547997,0.00009600243],"domain_scores_gemma":[0.999697,0.00001169614,0.00008246933,0.0001268548,0.00005359683,0.00002835388],"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":[0.0001427737,0.00003510013,0.0007009896,0.00003028402,0.0000201177,1.23953e-8,0.000009331096,0.000003450241,0.9956468,0.000638257,0.0001576159,0.002615309],"study_design_scores_gemma":[0.0004670039,0.00006026865,0.0006062181,0.000001501606,0.000009277262,0.000002279841,0.00001081697,0.00002007419,0.9481848,0.00009753631,0.05045604,0.00008416571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808736,0.0003319728,0.0183227,0.00006041411,0.00007632985,0.0002127633,0.0000228647,0.000006861374,0.00009245988],"genre_scores_gemma":[0.9914381,0.0000388005,0.006659077,0.0001188576,0.0001014452,0.00004681846,0.001382653,0.000009104851,0.0002051298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05029842,"threshold_uncertainty_score":0.307439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008513565123104168,"score_gpt":0.2158830373701962,"score_spread":0.2073694722470921,"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."}}