{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001236878,0.0001312962,0.0001380182,0.00003678531,0.0002705377,0.00004788742,0.000906912,0.0001601315,0.00002527921],"category_scores_gemma":[0.0002266105,0.0001199393,0.00003129481,0.00002537845,0.0001954587,0.000009305637,0.0006411672,0.00008360239,0.0000403673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007470065,"about_ca_system_score_gemma":0.000121549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000784237,"about_ca_topic_score_gemma":0.000009906506,"domain_scores_codex":[0.9990454,0.000073656,0.0001586957,0.0004468146,0.00007819984,0.000197197],"domain_scores_gemma":[0.998778,0.00002090242,0.0001468276,0.0009011865,0.00009559935,0.00005742161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003621868,0.0001947655,0.007974886,0.0000400118,0.0004293532,0.000009956759,0.00002123904,0.01078883,0.9455109,0.0222089,0.003332053,0.009126917],"study_design_scores_gemma":[0.006511173,0.003062694,0.04501201,0.0001306961,0.0001774866,0.0001797768,0.00003533669,0.3321099,0.231867,0.3027233,0.07553856,0.002652138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3384347,0.0003525106,0.6547449,0.001337444,0.0002452384,0.0008748109,0.0006460687,0.00005930631,0.003305032],"genre_scores_gemma":[0.9031696,0.00001018544,0.09362002,0.0001820073,0.0002887422,0.00002767069,0.002364958,0.00001533007,0.0003214461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7136439,"threshold_uncertainty_score":0.4890982,"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."}}