{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063156,0.000180039,0.0001564396,0.00004085005,0.00003568173,0.00004498133,0.00008565335,0.0002416153,0.0000161173],"category_scores_gemma":[0.00005790108,0.0001667657,0.00005772007,0.0001114813,0.00009388175,0.00001715107,0.00008600198,0.0001662699,0.00000784777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001484778,"about_ca_system_score_gemma":0.00002080358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002190994,"about_ca_topic_score_gemma":0.00000144907,"domain_scores_codex":[0.9989906,0.00002460986,0.0001735275,0.0005252364,0.00004709555,0.0002389333],"domain_scores_gemma":[0.9996764,0.00002216356,0.00003085141,0.000172391,0.00002810439,0.00007005526],"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":[0.00003661556,0.00001425798,0.00009420663,0.00003103162,0.00006172016,0.000002506908,0.00002989491,0.00001224794,0.9949327,0.0005163331,0.00015329,0.004115207],"study_design_scores_gemma":[0.0001572075,0.00006519949,0.0001350479,0.00001840886,0.00001823226,0.00003367533,0.00001133238,0.0002654181,0.9644637,0.00009277686,0.03453187,0.0002070989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970195,0.001671471,0.0267322,0.0005723949,0.0004579364,0.0001051842,0.00006147943,0.00007985758,0.0001244587],"genre_scores_gemma":[0.9969068,0.0003236963,0.0007188931,0.0002228166,0.0007857837,0.00002115027,0.0008173664,0.00002925002,0.0001742029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03437858,"threshold_uncertainty_score":0.6800506,"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."}}