{"id":"W3002766919","doi":"10.1038/s41598-020-58002-w","title":"Nanodisc technology facilitates identification of monoclonal antibodies targeting multi-pass membrane proteins","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada); University of British Columbia","funders":"CIHR Skin Research Training Centre; Canadian Institutes of Health Research; Mitacs; Government of Canada; Amgen","keywords":"Nanodisc; Membrane protein; Monoclonal antibody; Membrane; Flow cytometry; Chemistry; Integral membrane protein; Antibody; Cell biology; Biology; Biochemistry; Molecular biology; Immunology","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":[0.0003171125,0.0002652149,0.0003981407,0.0004631218,0.000290006,0.0006828747,0.0004187196,0.0006355715,0.0008292157],"category_scores_gemma":[0.0004040433,0.0001647089,0.0002474782,0.0002697542,0.0002519961,0.0004566,0.0003190761,0.0005133367,0.0006551763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004476125,"about_ca_system_score_gemma":0.0002491301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000451067,"about_ca_topic_score_gemma":0.0007360199,"domain_scores_codex":[0.9996547,0.0000502468,0.00003244815,0.00006297783,0.0001603884,0.00003931929],"domain_scores_gemma":[0.9997105,0.00009827846,0.0000522429,0.00003378132,0.00007476395,0.00003049642],"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":[0.00003517874,0.00002274505,0.0001143313,0.00006236112,0.00000448174,0.00006029003,0.00002683285,0.00006862367,0.9971137,0.0003313464,0.0001204025,0.00203955],"study_design_scores_gemma":[0.000004039374,0.00006908176,0.0007332112,0.000006110643,0.000008664905,0.0002511537,0.00003047773,0.00117984,0.992918,0.0001230743,0.004671525,0.000004909239],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8484432,0.006504867,0.1322336,0.000906623,0.0003047158,0.0001677026,0.001373009,0.000927202,0.009139033],"genre_scores_gemma":[0.8997164,0.00457251,0.08849104,0.0004946849,0.00005362101,0.0001511407,0.001055298,0.00007829464,0.005386927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008292157,"threshold_uncertainty_score":0.003247678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607226331904958,"score_gpt":0.3109151567814569,"score_spread":0.2748428934624074,"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."}}