{"id":"W2952228524","doi":"10.1101/669861","title":"Chemical precision glyco-mutagenesis by glycosyltransferase engineering in living cells","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Chemistry, Engineering and Medicine for Human Health, Stanford University; University of California, San Francisco; Canadian Institutes of Health Research; Defense Threat Reduction Agency; Universidad de Zaragoza; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Alexander von Humboldt-Stiftung; National Institutes of Health; National Science Foundation","keywords":"Glycosyltransferase; Glycan; Glycome; Glycosylation; Context (archaeology); Biochemistry; Mutagenesis; Bioorthogonal chemistry; Protein engineering; Function (biology); Living cell; Computational biology; Biosynthesis; Biology; Chemistry; Enzyme; Cell biology; Glycoprotein; Gene; Mutation; Click chemistry; Combinatorial chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004307938,0.0003851033,0.0002547365,0.0001680665,0.0001622109,0.0005134666,0.0003226887,0.0004980709,0.0008337045],"category_scores_gemma":[0.0002302974,0.0002467784,0.0002264065,0.0001667033,0.000510772,0.0002865242,0.0004915946,0.0008355841,0.0006681621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004096223,"about_ca_system_score_gemma":0.0002739995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004033298,"about_ca_topic_score_gemma":0.0004570283,"domain_scores_codex":[0.9997576,0.00004903773,0.00002305062,0.00005612786,0.00009041587,0.000023762],"domain_scores_gemma":[0.9998467,0.00003664246,0.00004771855,0.00004353259,0.00001164333,0.0000136978],"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":[0.00001740603,0.000009440174,0.00005866786,0.00002036741,0.000003649037,0.00002403253,0.00001009414,0.0002715363,0.9968184,0.001226612,0.000068862,0.001470905],"study_design_scores_gemma":[0.000008041045,0.00003562152,0.0001116449,0.000002109009,0.000004335584,0.00005252734,0.000004644036,0.00119126,0.9931057,0.0003163444,0.005163669,0.000004052428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6952707,0.001840068,0.2900195,0.0007894945,0.0003400986,0.0002528823,0.001019559,0.001619442,0.008848289],"genre_scores_gemma":[0.8779825,0.001668432,0.111229,0.0001522568,0.00003061986,0.0001662692,0.0006493391,0.0003022457,0.007819336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008337045,"threshold_uncertainty_score":0.002972007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006727737951409023,"score_gpt":0.2144306354605859,"score_spread":0.2077028975091768,"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."}}