{"id":"W2750023881","doi":"10.1371/journal.pcbi.1005722","title":"Data driven flexible backbone protein design","year":2017,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Protein design; Computer science; Protein structure prediction; Protein engineering; Protein structure; Representation (politics); Artificial intelligence; Protein Data Bank (RCSB PDB); Machine learning; Computational biology; Biology","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":[0.001261266,0.0007001811,0.0009211189,0.000420105,0.0004471771,0.0006821742,0.001607297,0.0009912334,0.003555785],"category_scores_gemma":[0.002245021,0.0005429638,0.0006546776,0.0006191729,0.0007718285,0.001090752,0.001338107,0.001321993,0.001120962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006831562,"about_ca_system_score_gemma":0.001136268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000744385,"about_ca_topic_score_gemma":0.001090072,"domain_scores_codex":[0.9995184,0.0001250062,0.00002235205,0.0001017025,0.0001754117,0.00005706924],"domain_scores_gemma":[0.9992524,0.000246559,0.00006201309,0.0002105558,0.0001644451,0.00006401791],"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":[0.0001534786,0.00007308929,0.0009449508,0.0001569104,0.00004372984,0.0001291268,0.00006190809,0.8711779,0.01134634,0.0375726,0.003270826,0.07506923],"study_design_scores_gemma":[0.00003281395,0.00004052944,0.00004843103,0.000005716693,0.000006521027,0.00002473065,0.000009125532,0.9802924,0.002282323,0.01477952,0.002472076,0.000005896563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04328959,0.0003852881,0.9498277,0.0002622563,0.00006203262,0.0001006002,0.0002732571,0.001235215,0.004564208],"genre_scores_gemma":[0.4539693,0.0004053936,0.5404125,0.0002394285,0.00003131595,0.0004118945,0.0008592477,0.0005054463,0.003165425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003555785,"threshold_uncertainty_score":0.01189524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06234078242518868,"score_gpt":0.313820222569503,"score_spread":0.2514794401443143,"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."}}