{"id":"W4385948466","doi":"10.1038/s41589-023-01405-3","title":"A high-throughput screening platform for enzymes active on mucin-type O-glycoproteins","year":2023,"lang":"en","type":"article","venue":"Nature Chemical Biology","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Victoria; University of British Columbia Hospital; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Glycomics Network; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Glycoprotein; Glycosylation; Biochemistry; Glycosyltransferase; Enzyme; Protein engineering; Directed evolution; Förster resonance energy transfer; High-throughput screening; Mucin; Glycan; Escherichia coli; Chemistry; Biology; Fluorescence; Gene; Mutant","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":[0.0004651678,0.001466032,0.001216174,0.0009946196,0.0004670853,0.001053543,0.0008857648,0.0008886991,0.001694933],"category_scores_gemma":[0.0003086895,0.0005058178,0.0008038633,0.0006413082,0.0002420128,0.000534739,0.00104005,0.0009126994,0.002203984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003053344,"about_ca_system_score_gemma":0.0004987213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006939542,"about_ca_topic_score_gemma":0.001298657,"domain_scores_codex":[0.9992441,0.00009382659,0.00003662072,0.000117768,0.0003902122,0.0001175254],"domain_scores_gemma":[0.9998295,0.00004224687,0.00002302568,0.00002280735,0.00003113987,0.00005131301],"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":[0.0001099806,0.0001025528,0.0001476203,0.00004449687,0.00001762337,0.00005497933,0.000009278292,0.000125894,0.9938928,0.0000706792,0.0003872361,0.00503687],"study_design_scores_gemma":[0.00005033256,0.0004605055,0.001781159,0.000009125743,0.00005248874,0.0004006794,0.0000214257,0.002818491,0.9895096,0.00007199802,0.004794474,0.00002970185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7991986,0.004091798,0.1736055,0.001059059,0.0002715149,0.001629598,0.009464175,0.004523842,0.006155893],"genre_scores_gemma":[0.7364011,0.004883871,0.2076538,0.000595254,0.0001233655,0.001288875,0.03086209,0.0003686604,0.01782309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001694933,"threshold_uncertainty_score":0.00567019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370000155991877,"score_gpt":0.3340733860505111,"score_spread":0.3103733844905923,"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."}}