{"id":"W3157774880","doi":"10.1016/j.jbc.2021.100747","title":"Structural genomics and the Protein Data Bank","year":2021,"lang":"en","type":"review","venue":"Journal of Biological Chemistry","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Argonne National Laboratory; National Institute of Allergy and Infectious Diseases; Office of Science; U.S. Department of Health and Human Services; National Institutes of Health; University of Chicago; U.S. Department of Energy","keywords":"Structural genomics; Genomics; Protein Data Bank; Computational biology; Computer science; Biology; Genetics; Genome; Protein structure; Gene; Biochemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01078312,0.001608012,0.001837566,0.006263598,0.00161663,0.006826541,0.002872243,0.003160429,0.03820775],"category_scores_gemma":[0.02218416,0.001143251,0.001249728,0.01392827,0.002593446,0.009316027,0.003692665,0.006396071,0.04785442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004467986,"about_ca_system_score_gemma":0.006716042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004567096,"about_ca_topic_score_gemma":0.002137746,"domain_scores_codex":[0.9924581,0.002535649,0.0008569241,0.001083633,0.002754883,0.0003109046],"domain_scores_gemma":[0.9852468,0.004196773,0.001551805,0.003613007,0.004301356,0.001090316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00033804,0.0001027242,0.001506117,0.001021567,0.0001174411,0.0002004365,0.0001319484,0.002484629,0.002107121,0.2440566,0.5720431,0.1758903],"study_design_scores_gemma":[0.000088011,0.00003473324,0.0008197585,0.0003623764,0.00003464772,0.0002321527,0.00005769196,0.004181009,0.001238709,0.108647,0.8842528,0.00005095289],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007091761,0.07759642,0.3337561,0.1717851,0.01504076,0.0009509696,0.1240327,0.04240449,0.2273418],"genre_scores_gemma":[0.0564334,0.1013325,0.4221931,0.03627516,0.007520867,0.002307584,0.2995617,0.007202073,0.06717352],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.03820775,"threshold_uncertainty_score":0.1278176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04441926835454992,"score_gpt":0.3080082416797641,"score_spread":0.2635889733252142,"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."}}