{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004468301,0.0001254396,0.0002681074,0.00001947039,0.00004735254,0.00002810962,0.0004333446,0.00007792299,0.000002624017],"category_scores_gemma":[0.0004396735,0.00008409848,0.0005163334,0.0002081141,0.0003424213,0.000008042577,0.0001260615,0.0001847054,5.638873e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007196295,"about_ca_system_score_gemma":0.000465233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000186305,"about_ca_topic_score_gemma":0.000001543864,"domain_scores_codex":[0.9987639,0.00003465192,0.000382969,0.0001555112,0.0004247949,0.0002381269],"domain_scores_gemma":[0.998677,0.00003808796,0.0005060676,0.0002558679,0.0003765283,0.0001464862],"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":[0.0004261812,0.0001026313,0.0007909753,0.00002112343,0.00008955025,5.700206e-7,0.00005056354,0.0002618004,0.9950314,0.000006759956,0.00281295,0.000405481],"study_design_scores_gemma":[0.001139287,0.0005839999,0.000103164,0.00003126385,0.0000288644,0.000006740687,0.0002087039,0.0006585746,0.9952133,0.00006280457,0.001859162,0.0001041822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918951,0.00009918453,0.006717146,0.0008877406,0.00006396771,0.0002675549,0.00002369815,0.000002549914,0.00004299189],"genre_scores_gemma":[0.9893362,0.00002375633,0.009891782,0.0003146602,0.0002345399,0.00001653344,0.000009524002,0.00001900457,0.0001539789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003174636,"threshold_uncertainty_score":0.3429436,"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."}}