{"id":"W4225811022","doi":"10.1021/acs.bioconjchem.2c00043","title":"Exo-Enzymatic Cell-Surface Glycan Labeling for Capturing Glycan–Protein Interactions through Photo-Cross-Linking","year":2022,"lang":"en","type":"article","venue":"Bioconjugate Chemistry","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Canada Foundation for Innovation; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Glycan; Chemistry; Glycoconjugate; Diazirine; Sialyltransferase; Biochemistry; Protein–protein interaction; Enzyme; Context (archaeology); Epitope; Oligosaccharide; Computational biology; Glycoprotein; Biology; Antigen","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003700439,0.000312744,0.0002390116,0.0000291162,0.0008067397,0.0001478194,0.0005131685,0.0001480307,0.0007548716],"category_scores_gemma":[0.0001113904,0.0003564507,0.00022076,0.0001955763,0.0001090071,0.00001908245,0.0004880145,0.0004298501,0.00001950453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001580134,"about_ca_system_score_gemma":0.0002341188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001405558,"about_ca_topic_score_gemma":0.00001187605,"domain_scores_codex":[0.9977858,0.00006213308,0.0004583478,0.0007267392,0.0003628293,0.0006041227],"domain_scores_gemma":[0.9987982,0.00003277597,0.0002189402,0.0006204338,0.0001804382,0.0001492456],"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":[0.0002433994,0.0001094788,0.0001639677,0.0003176532,0.00005112233,0.000007207531,0.0001154965,0.001327486,0.9968415,0.00002595992,0.0006327903,0.0001639579],"study_design_scores_gemma":[0.001310089,0.00009399647,0.00000850565,0.00003642161,0.00001393444,0.00003117348,0.0004217729,0.0006976716,0.8814031,0.0002048637,0.1154076,0.0003709204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904143,0.0009170762,0.001953103,0.000203437,0.0002122693,0.0009456614,0.0002470701,0.00006653656,0.005040585],"genre_scores_gemma":[0.9801019,0.00003434467,0.003335391,0.0002142487,0.000316082,0.000604252,0.000764133,0.00007985339,0.01454975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1154384,"threshold_uncertainty_score":0.9998887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236976712384038,"score_gpt":0.3007899097565022,"score_spread":0.2784201426326618,"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."}}