{"id":"W2065154251","doi":"10.1016/j.jsb.2011.03.018","title":"Strategies for the structural analysis of multi-protein complexes: Lessons from the 3D-Repertoire project","year":2011,"lang":"en","type":"article","venue":"Journal of Structural Biology","topic":"Enzyme Structure and Function","field":"Materials Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"European Social Fund; Institute of Genetics; European Synchrotron Radiation Facility; Agence Nationale de la Recherche","keywords":"In silico; Structural biology; Computational biology; Repertoire; Protein structure; Crystallography; Chemistry; Protein crystallization; Nuclear magnetic resonance spectroscopy; Biological system; Biochemistry; Biology; Physics; Stereochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003456995,0.0001940602,0.0005198931,0.0001196966,0.0002991176,0.000050472,0.0006870811,0.0001278289,0.0004570311],"category_scores_gemma":[0.0001185403,0.00008052598,0.0003338965,0.0003004218,0.0005007759,0.0002217473,0.00006705615,0.0002195628,7.907584e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002796287,"about_ca_system_score_gemma":0.0001229504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001991955,"about_ca_topic_score_gemma":0.001224656,"domain_scores_codex":[0.9985462,0.0001986238,0.0006468138,0.0002031269,0.000153731,0.0002514754],"domain_scores_gemma":[0.9980515,0.0003806657,0.0009040656,0.0003071205,0.0003226419,0.00003405775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005255367,0.000006536549,0.003666949,0.00001170575,0.0009658529,0.000001911555,0.00333694,0.0002680421,0.9789325,0.008029592,0.0001584111,0.004096016],"study_design_scores_gemma":[0.003415297,0.002080047,0.6380283,0.00008085156,0.005534697,0.0001774087,0.02475616,0.01025908,0.2544234,0.05877697,0.001658312,0.000809463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940068,0.0008785304,0.003163087,0.0003078204,0.0009307186,0.0002845805,0.0003785748,0.00001111435,0.00003874027],"genre_scores_gemma":[0.9910362,0.00001281915,0.008488382,0.0001005001,0.0003116636,0.0000068413,0.0000218519,0.000008666202,0.00001309947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7245091,"threshold_uncertainty_score":0.500417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08598154855024262,"score_gpt":0.3340619285164289,"score_spread":0.2480803799661863,"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."}}