{"id":"W2345741266","doi":"10.1126/science.aad8036","title":"Design of structurally distinct proteins using strategies inspired by evolution","year":2016,"lang":"en","type":"article","venue":"Science","topic":"Enzyme Structure and Function","field":"Materials Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium","funders":"Basic Energy Sciences; National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; Office of Science; U.S. Department of Energy","keywords":"Angstrom; Protein design; High resolution; Computational biology; Protein structure; Resolution (logic); Protein engineering; Computer science; Process (computing); Biology; Nanotechnology; Chemistry; Crystallography; Materials science; Artificial intelligence; Biochemistry; Programming language","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":[0.0004809056,0.00009221793,0.0001020397,0.00006963799,0.0002128464,0.00007122893,0.0003231392,0.00003437954,0.0001302324],"category_scores_gemma":[0.0001322718,0.00005457407,0.00001637444,0.0003614553,0.0008298627,0.001107711,0.00005492625,0.00002773575,0.00001143419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131048,"about_ca_system_score_gemma":0.0003676855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001112114,"about_ca_topic_score_gemma":0.00001356313,"domain_scores_codex":[0.9988132,0.00004568195,0.0001830941,0.0002928183,0.0004081792,0.0002570041],"domain_scores_gemma":[0.9994037,0.00003033774,0.0001441208,0.0002186089,0.0001486768,0.00005451817],"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":[0.00001766376,0.000004171369,0.000335709,0.000006216728,5.388234e-7,2.711786e-7,0.00007142164,0.0001303859,0.9975176,0.001202291,0.00001815355,0.0006956081],"study_design_scores_gemma":[0.0001646818,0.0001184964,0.0093902,0.00003670952,0.000004971657,0.000006729729,0.00007959906,0.0005085862,0.9830431,0.006511611,0.00002244508,0.0001128348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6916074,0.00003725911,0.3077474,0.0000203486,0.0002809879,0.0001345385,0.00001094419,0.00003283093,0.0001282699],"genre_scores_gemma":[0.989191,9.252196e-7,0.0107055,0.000006645486,0.00003813511,0.000003937935,3.521332e-7,0.000004551812,0.00004897304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2975836,"threshold_uncertainty_score":0.3057663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008220651702262,"score_gpt":0.2509389827373488,"score_spread":0.2308567762203262,"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."}}