{"id":"W4382247824","doi":"10.1016/j.cell.2023.05.041","title":"Discovery of deaminase functions by structure-based protein clustering","year":2023,"lang":"en","type":"article","venue":"Cell","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":229,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Genetics","funders":"Steno Diabetes Center Aarhus; National Key Research and Development Program of China; National Natural Science Foundation of China; Novo Nordisk Fonden; Chinese Academy of Sciences; China Postdoctoral Science Foundation; National Postdoctoral Program for Innovative Talents","keywords":"Biology; Cytosine deaminase; Cytidine deaminase; Computational biology; DNA; Cytosine; Genetics; Protein structure; Cytidine; Substrate specificity; Gene; Biochemistry; Enzyme","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":[0.00003001144,0.00006843545,0.00005728402,0.00002614247,0.00002234135,0.000008032358,0.0000622812,0.00005123186,0.000008205131],"category_scores_gemma":[0.00001039922,0.00006804211,0.00003929985,0.00007957767,0.0000179024,0.000001307196,0.00003922959,0.00003240363,0.000005065187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003451156,"about_ca_system_score_gemma":0.00002344638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007650862,"about_ca_topic_score_gemma":0.00001296204,"domain_scores_codex":[0.9996114,0.000007616471,0.00008479913,0.0001320669,0.00004929907,0.0001148036],"domain_scores_gemma":[0.9997656,0.000003665918,0.00002099866,0.0001721906,0.00001388648,0.00002368611],"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":[0.00001063375,0.00001035323,0.00009761268,0.00005517653,0.000004758106,6.807304e-7,0.000006994114,0.005096531,0.9925403,8.439448e-7,0.0019513,0.0002248002],"study_design_scores_gemma":[0.0002140993,0.00006962764,0.0003106747,0.000007392332,0.000005479608,4.447999e-7,0.00003189079,0.001166808,0.9930622,0.000005251376,0.005047699,0.00007845715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444567,0.0002065703,0.05478283,0.00001961505,0.0000880998,0.0000966969,0.00007496014,0.00001444599,0.0002600785],"genre_scores_gemma":[0.9962493,0.000006355114,0.0003250561,0.00001468037,0.00004860539,0.00001022904,0.000233141,0.00001384837,0.00309885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05445777,"threshold_uncertainty_score":0.2774677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004559038332760087,"score_gpt":0.2378273530537814,"score_spread":0.2332683147210213,"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."}}