{"id":"W2947293772","doi":"10.3390/antibiotics8020065","title":"Development of a High Throughput Screen for the Identification of Inhibitors of Peptidoglycan O-Acetyltransferases, New Potential Antibacterial Targets","year":2019,"lang":"en","type":"article","venue":"Antibiotics","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canadian Glycomics Network; McMaster University; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Scheme for Promotion of Academic and Research Collaboration; University of Toronto","keywords":"Peptidoglycan; Microbiology; Neisseria gonorrhoeae; Sydnone; Lytic cycle; Biochemistry; Escherichia coli; High-throughput screening; Enzyme; Biology; Ribitol; Chemistry; Virology","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":[0.0004116254,0.0009133514,0.001380163,0.0008789925,0.0003054866,0.0007903806,0.0005096367,0.0007110874,0.002225783],"category_scores_gemma":[0.0004235047,0.0004193082,0.0005202231,0.0007674536,0.0001984482,0.0003332006,0.0004387504,0.0008423919,0.0009700389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003067293,"about_ca_system_score_gemma":0.0005582916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008540872,"about_ca_topic_score_gemma":0.002437479,"domain_scores_codex":[0.9995865,0.0000636698,0.00003182235,0.00006465063,0.0002019853,0.00005142908],"domain_scores_gemma":[0.9997531,0.00007160538,0.00003818976,0.00002131294,0.00007068341,0.00004525559],"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":[0.0002424045,0.0006468971,0.0005083857,0.0002525064,0.00005891419,0.0002527309,0.00003127867,0.001205773,0.9838929,0.0001045346,0.0006042033,0.01219936],"study_design_scores_gemma":[0.0004065974,0.01258198,0.009222622,0.00005720961,0.0003624788,0.001477076,0.00008877512,0.006674489,0.9548811,0.0001302432,0.01405179,0.00006559046],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940898,0.01023278,0.02862181,0.0007436796,0.0001730589,0.001999515,0.007533614,0.001148989,0.008648507],"genre_scores_gemma":[0.9317755,0.0116821,0.03083455,0.0005346749,0.00008829952,0.001596191,0.009836208,0.0001182263,0.01353422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002225783,"threshold_uncertainty_score":0.007445991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120206476459271,"score_gpt":0.2614979673819579,"score_spread":0.2502959026173651,"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."}}