{"id":"W2065237193","doi":"10.1021/ja511237n","title":"Genetically Encoded Fragment-Based Discovery of Glycopeptide Ligands for Carbohydrate-Binding Proteins","year":2015,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Glycomics Centre; University of Calgary; University of Alberta","funders":"Biological and Environmental Research; Alberta Innovates; National Institute of General Medical Sciences; Alberta Glycomics Centre; National Institutes of Health; Science Foundation Ireland; Canada Foundation for Innovation; University of Alberta; Argonne National Laboratory; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures","keywords":"Chemistry; Concanavalin A; Glycopeptide; Lectin; Glycan; Binding selectivity; Peptide; Biochemistry; Computational biology; Glycoprotein; In vitro; Biology","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":[0.0001534294,0.0003714841,0.000280839,0.0001963586,0.00008832406,0.0003352344,0.0003623907,0.000308526,0.0004926121],"category_scores_gemma":[0.0001361078,0.0001173606,0.0002518938,0.00020821,0.0002398427,0.0001607741,0.0002894022,0.0006582936,0.0002557405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003580142,"about_ca_system_score_gemma":0.0002166946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004671376,"about_ca_topic_score_gemma":0.0007429183,"domain_scores_codex":[0.9998926,0.00001426205,0.000005033414,0.0000192542,0.00004813135,0.00002075437],"domain_scores_gemma":[0.9999528,0.000009148238,0.00001365556,0.000004983192,0.00000666287,0.00001270027],"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":[0.0000395638,0.00002960706,0.0001386629,0.00002329792,0.000006532078,0.00006380273,0.000008007895,0.0004411394,0.9945353,0.0003972905,0.00006781235,0.004248914],"study_design_scores_gemma":[0.00002734361,0.0004684934,0.0006772428,0.00000366193,0.00001975459,0.0002966744,0.000008427072,0.002457952,0.9904518,0.0001189573,0.005461352,0.000008255563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8943315,0.003658049,0.09610501,0.0004393724,0.0000866214,0.0002469912,0.0007936226,0.0004486458,0.003890328],"genre_scores_gemma":[0.9196414,0.00286736,0.07239326,0.0001976148,0.00002046228,0.0001001948,0.001015113,0.00006104239,0.003703471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004926121,"threshold_uncertainty_score":0.00259757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795818338912528,"score_gpt":0.2855179897384922,"score_spread":0.2675598063493669,"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."}}