{"id":"W2167581535","doi":"10.1002/chem.201405259","title":"Specificity of Furanoside–Protein Recognition through Antibody Engineering and Molecular Modeling","year":2014,"lang":"en","type":"article","venue":"Chemistry - A European Journal","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Glycomics Centre; University of Alberta","funders":"Eurostars; Natural Sciences and Engineering Research Council of Canada; National Institute of General Medical Sciences; Western Economic Diversification Canada; Alberta Glycomics Centre; Science Foundation Ireland; National Institutes of Health; University of Alberta","keywords":"Molecular recognition; Ring (chemistry); Hydrogen bond; Chemistry; Polysaccharide; Antibody; Computational biology; Mutant; Biology; Stereochemistry; Biochemistry; Molecule; Genetics; Gene","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":[0.0002533053,0.0005694697,0.0004969472,0.0002216815,0.0002212433,0.0004787256,0.0006440007,0.0004392513,0.001298676],"category_scores_gemma":[0.0002607974,0.0002323261,0.000414706,0.0002096587,0.0001733662,0.0002730284,0.0002498123,0.0005566236,0.0003420568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004347159,"about_ca_system_score_gemma":0.0004852254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001766907,"about_ca_topic_score_gemma":0.001340516,"domain_scores_codex":[0.9998931,0.00002862496,0.000005525364,0.00001876558,0.00002709283,0.00002695799],"domain_scores_gemma":[0.9999411,0.00002486096,0.000009729687,0.00000419114,0.00001285989,0.000007229386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004089968,0.0004047849,0.001739951,0.0003275802,0.00009097694,0.0004443132,0.0001590288,0.4164752,0.5453568,0.01152719,0.0005953542,0.02246984],"study_design_scores_gemma":[0.00005411028,0.0001426168,0.0003444262,0.00000877033,0.00002305237,0.00005019817,0.00003079846,0.9200723,0.07613181,0.0009584851,0.002169681,0.00001384155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8756891,0.000629066,0.1169826,0.0001872576,0.00003416494,0.0001253125,0.0002180653,0.0005572934,0.005577106],"genre_scores_gemma":[0.9290432,0.0008195286,0.06807271,0.00003518819,0.000008039442,0.0001500514,0.0004409784,0.00008748151,0.001342726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001766907,"threshold_uncertainty_score":0.004344463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729171682533123,"score_gpt":0.248579058431014,"score_spread":0.2312873416056828,"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."}}