{"id":"W2135573413","doi":"10.1016/j.str.2015.07.021","title":"Prediction of Stable Globular Proteins Using Negative Design with Non-native Backbone Ensembles","year":2015,"lang":"en","type":"article","venue":"Structure","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Protein design; Context (archaeology); Globular protein; Protein stability; Stability (learning theory); Protein domain; Statistical potential; Protein engineering; Protein structure; Computer science; Biological system; Algorithm; Computational biology; Chemistry; Protein structure prediction; Biology; Crystallography; Machine learning; Biochemistry","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":[0.00009136635,0.0001909877,0.0001941276,0.00003971285,0.00005822791,0.00001456687,0.0001283781,0.0001945327,0.000007949851],"category_scores_gemma":[0.00007735766,0.0001461356,0.00003470246,0.0001562156,0.0001292554,0.00001132249,0.00006120271,0.0001117034,2.660514e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003516352,"about_ca_system_score_gemma":0.0002832536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008125736,"about_ca_topic_score_gemma":0.0000372036,"domain_scores_codex":[0.9990642,0.00006774209,0.0001643509,0.0002891425,0.0002123761,0.0002021696],"domain_scores_gemma":[0.9991468,0.000006988842,0.0001593721,0.0002793514,0.000317362,0.0000901785],"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":[0.0006093868,0.00001680289,0.001624303,0.0000380637,0.0001520993,0.000005626706,0.0004625445,0.01447405,0.9820036,0.00009513957,0.0002145895,0.0003038067],"study_design_scores_gemma":[0.001213435,0.000930681,0.001160725,0.00004052035,0.0000548734,0.0000517855,0.0003432678,0.002944176,0.9887639,0.004040977,0.0002527837,0.0002028247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8117678,0.000185309,0.1868325,0.00001539639,0.00008036626,0.0005915897,0.0002300343,0.00001069187,0.000286351],"genre_scores_gemma":[0.9083749,0.000006036112,0.09127211,0.00002938835,0.000140902,0.000009302838,0.00007307844,0.00002265952,0.00007160164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09660717,"threshold_uncertainty_score":0.5959237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122382860981097,"score_gpt":0.2344081788927984,"score_spread":0.2131843502829875,"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."}}