{"id":"W4319337146","doi":"10.1021/acs.jctc.2c01270","title":"Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Dihedral angle; Cartesian coordinate system; Orthogonal coordinates; Python (programming language); Bond length; Computer science; Algorithm; Geometry; Mathematics; Crystallography; Physics; Chemistry; Molecule","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":[0.002094933,0.001713494,0.001679671,0.001232279,0.0009036405,0.002064815,0.002817465,0.001489175,0.002166377],"category_scores_gemma":[0.009810989,0.001030914,0.001596852,0.001509017,0.001482992,0.00236738,0.002792949,0.002659669,0.001656811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00158428,"about_ca_system_score_gemma":0.002286208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008952348,"about_ca_topic_score_gemma":0.006970595,"domain_scores_codex":[0.9990311,0.0002890406,0.00004850072,0.0003204505,0.0002305492,0.00008045434],"domain_scores_gemma":[0.9972981,0.0008808122,0.0003841768,0.0007037981,0.0005369836,0.0001962157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001214069,0.0000677723,0.004351599,0.0001316821,0.00007098725,0.0001333796,0.0001591663,0.8972039,0.003916816,0.01980318,0.004033924,0.07000627],"study_design_scores_gemma":[0.000006509392,0.00001599872,0.00008979368,0.000008085779,0.000003929431,0.00001615804,0.000009201143,0.9933798,0.000939663,0.004931666,0.0005922042,0.000006978292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03909525,0.0003379993,0.9549606,0.0002780661,0.00007781073,0.00003766717,0.0003505646,0.003925732,0.0009364117],"genre_scores_gemma":[0.3943745,0.0004979303,0.5998451,0.0002588945,0.00009029799,0.0001719917,0.002109241,0.001378516,0.001273471],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008952348,"threshold_uncertainty_score":0.01780051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006181310799758311,"score_gpt":0.2671492072117828,"score_spread":0.2609678964120244,"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."}}