{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008698837,0.000761554,0.0006604553,0.0005118686,0.0004113043,0.000981092,0.001139372,0.0007020847,0.0009772247],"category_scores_gemma":[0.000973062,0.0005541948,0.0007265602,0.0004679657,0.0009425845,0.0008846704,0.001145354,0.001277134,0.0004722216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006967159,"about_ca_system_score_gemma":0.0005152487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000198287,"about_ca_topic_score_gemma":0.0003770767,"domain_scores_codex":[0.999569,0.0001094578,0.00004915554,0.0001227411,0.00009879753,0.00005080411],"domain_scores_gemma":[0.9996452,0.0001098959,0.00008812335,0.00008078228,0.00002971624,0.00004636048],"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":[0.0003107208,0.0003572001,0.001483569,0.0006430669,0.0001920952,0.0005529053,0.0005135877,0.03919868,0.7901132,0.08312316,0.0007999823,0.08271193],"study_design_scores_gemma":[0.0005857864,0.0025233,0.001909613,0.0001053963,0.0003176354,0.001799419,0.000392796,0.1730266,0.6613595,0.0447645,0.1130301,0.000185315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4933443,0.002055956,0.4939972,0.000523169,0.0002036974,0.00056037,0.0001896249,0.001076476,0.008049223],"genre_scores_gemma":[0.5091278,0.001521057,0.4842894,0.0002578101,0.0000211232,0.0007022878,0.000340148,0.000242828,0.003497625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001139372,"threshold_uncertainty_score":0.00505507,"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."}}