{"id":"W4398757348","doi":"10.1039/d4cb00024b","title":"Carbohydrate-active enzyme (CAZyme) discovery and engineering <i>via</i> (Ultra)high-throughput screening","year":2024,"lang":"en","type":"article","venue":"RSC Chemical Biology","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Glycomics Network; Canadian Institutes of Health Research","keywords":"Throughput; High-throughput screening; Identification (biology); Computational biology; Computer science; Virtual screening; Enzyme; Drug discovery; Protein engineering; Biochemical engineering; Biology; Biochemistry; Engineering","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":[0.001745607,0.001328697,0.001012868,0.001145262,0.0003468146,0.002097808,0.001020367,0.001183171,0.002071892],"category_scores_gemma":[0.001958381,0.0004640465,0.0008789753,0.001215175,0.0007526682,0.00168541,0.001306878,0.001544479,0.002622315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007890238,"about_ca_system_score_gemma":0.0005881514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004781762,"about_ca_topic_score_gemma":0.0008204889,"domain_scores_codex":[0.998826,0.0002084575,0.00008145868,0.0002247122,0.0005306609,0.0001287751],"domain_scores_gemma":[0.9994166,0.0001821231,0.000111562,0.00007469096,0.000159934,0.00005521765],"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":[0.0002186418,0.000226849,0.001623212,0.004443524,0.0002412098,0.0005321233,0.0001266385,0.004076553,0.7524893,0.02575925,0.02040532,0.1898574],"study_design_scores_gemma":[0.00002730683,0.0002729335,0.0008128615,0.0002199945,0.00008096442,0.0007711292,0.00004810259,0.005073729,0.8642241,0.003025481,0.1253765,0.00006686438],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1065067,0.1817592,0.6277519,0.005051042,0.002008935,0.00109546,0.006075333,0.004641717,0.06510986],"genre_scores_gemma":[0.4076267,0.2148671,0.345205,0.002472305,0.0006468051,0.0009637372,0.006496585,0.000666152,0.02105559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002097808,"threshold_uncertainty_score":0.009231806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006206611866911182,"score_gpt":0.2208324394052241,"score_spread":0.2146258275383129,"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."}}