{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009732057,0.001129046,0.0009104685,0.001361118,0.0004240763,0.001209533,0.000835111,0.0006834493,0.000834483],"category_scores_gemma":[0.000780742,0.0005237487,0.0008909489,0.0009026492,0.0003169155,0.000465424,0.001329842,0.001281938,0.0006987333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545043,"about_ca_system_score_gemma":0.0008365721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009398673,"about_ca_topic_score_gemma":0.001718473,"domain_scores_codex":[0.999126,0.0001362937,0.00008165865,0.0001486456,0.0003939811,0.0001133748],"domain_scores_gemma":[0.9995885,0.00009175573,0.00006584445,0.0001004156,0.00007879631,0.00007460733],"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":[0.00002840482,0.00006543089,0.0003119066,0.0000350368,0.00001228728,0.00004878482,0.000009314876,0.0001665775,0.9959339,0.00007284948,0.00004170543,0.003273803],"study_design_scores_gemma":[0.00003039425,0.0005261461,0.003366536,0.000009230856,0.00007535209,0.0004401219,0.00006131569,0.002435716,0.9898359,0.000165723,0.003034657,0.00001878087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8254962,0.001652227,0.1555955,0.001357599,0.0001413139,0.001801695,0.007778965,0.001580435,0.004596089],"genre_scores_gemma":[0.8338947,0.00259968,0.1496153,0.0002841393,0.00003174314,0.0008145044,0.008885694,0.0002676899,0.003606661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001361118,"threshold_uncertainty_score":0.005146861,"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."}}