{"id":"W2485748922","doi":"10.1016/j.jsb.2016.07.012","title":"Use of evolutionary information in the fitting of atomic level protein models in low resolution cryo-EM map of a protein assembly improves the accuracy of the fitting","year":2016,"lang":"en","type":"article","venue":"Journal of Structural Biology","topic":"Enzyme Structure and Function","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"Indo-French Centre for the Promotion of Advanced Research; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Interface (matter); Biological system; Protein structure prediction; Computer science; Algorithm; Data mining; Resolution (logic); Cryo-electron microscopy; Low resolution; Protein structure; High resolution; Physics; Artificial intelligence; Biology; Remote sensing; Nuclear magnetic resonance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103445,0.0001162712,0.0003035783,0.0001508194,0.00005837087,0.00001094333,0.0004298086,0.0001311912,0.00001145014],"category_scores_gemma":[0.001045014,0.00004663613,0.0001087555,0.0002017999,0.0002417753,0.0009650458,0.00009932892,0.0002136109,2.879369e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006897945,"about_ca_system_score_gemma":0.0001576578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000321651,"about_ca_topic_score_gemma":0.00006477414,"domain_scores_codex":[0.9978084,0.0004489321,0.001220201,0.00009068392,0.0002514408,0.0001803608],"domain_scores_gemma":[0.9966376,0.0003908728,0.002407149,0.0002210379,0.0003294752,0.00001389306],"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":[0.0003252238,0.000008255715,0.001720059,0.00009807303,0.000009690887,1.986804e-7,0.001110304,0.0007852705,0.990602,0.001747665,0.00001751345,0.003575773],"study_design_scores_gemma":[0.001094743,0.0004478253,0.2309097,0.0009151175,0.00002171468,0.00005668747,0.001129271,0.003737656,0.7373695,0.02417192,0.00002928123,0.0001166066],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981055,0.0001168658,0.000425036,0.0006054614,0.000194811,0.0004949215,0.00004836592,0.000002140209,0.000006882514],"genre_scores_gemma":[0.998167,0.000005625381,0.001722398,0.00002595774,0.00006096379,0.000006997481,0.000001637931,0.000003793125,0.000005605885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2532325,"threshold_uncertainty_score":0.1901766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984128170950349,"score_gpt":0.2536952354406365,"score_spread":0.223853953731133,"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."}}