{"id":"W4386121941","doi":"10.3389/fmolb.2023.1253689","title":"Exploring rigid-backbone protein docking in biologics discovery: a test using the DARPin scaffold","year":2023,"lang":"en","type":"article","venue":"Frontiers in Molecular Biosciences","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada","funders":"Alliance de recherche numérique du Canada","keywords":"Docking (animal); Scaffold; Protein engineering; Epitope; Scaffold protein; Computational biology; Protein design; Flexibility (engineering); Directed evolution; Computer science; Protein structure; Chemistry; Biology; Biochemistry; Genetics; Mutant; Antibody","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":[0.001085017,0.0006287648,0.0007763288,0.0003243728,0.000276969,0.0006973376,0.0006427465,0.0004823162,0.001243658],"category_scores_gemma":[0.001061579,0.0001965421,0.0003970235,0.0004317227,0.0003760812,0.0006129126,0.0006765914,0.0005777973,0.000287315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003223351,"about_ca_system_score_gemma":0.0004605264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006903562,"about_ca_topic_score_gemma":0.0009030616,"domain_scores_codex":[0.9997028,0.00007515355,0.00001533058,0.0000588297,0.0001029265,0.00004486417],"domain_scores_gemma":[0.9997306,0.0001284537,0.00002586488,0.00004461751,0.00003779676,0.00003260187],"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":[0.001051283,0.0006905736,0.008164338,0.0009313262,0.000181536,0.0007898344,0.0001540494,0.2808659,0.5978413,0.007652465,0.001019172,0.1006583],"study_design_scores_gemma":[0.0003497165,0.003719507,0.00399979,0.000049764,0.0001410998,0.000964819,0.0001564135,0.4919555,0.4874385,0.002540106,0.008598674,0.00008607292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9410471,0.001684066,0.05359718,0.0002229416,0.00002149601,0.00007370312,0.0001915051,0.000685674,0.00247624],"genre_scores_gemma":[0.9474854,0.001450687,0.04969628,0.00008372172,0.000006269023,0.00006742001,0.000430923,0.00005250556,0.0007265512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001243658,"threshold_uncertainty_score":0.005738139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1160323797302699,"score_gpt":0.3319717899345653,"score_spread":0.2159394102042954,"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."}}