{"id":"W1884499457","doi":"10.1093/protein/gzv055","title":"Engineering a genetically encoded competitive inhibitor of the KEAP1–NRF2 interaction via structure-based design and phage display","year":2015,"lang":"en","type":"article","venue":"Protein Engineering Design and Selection","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health; University of Toronto; University of Missouri","keywords":"KEAP1; Phage display; Ubiquitin; Transcription factor; Chemistry; Cysteine; Transcription (linguistics); Computational biology; Cell biology; Biology; Gene; Biochemistry; Enzyme","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000431453,0.000584692,0.0004650016,0.000331448,0.0002420165,0.0005718776,0.0007621839,0.0006566782,0.0008214352],"category_scores_gemma":[0.0003621084,0.0003840807,0.0004241936,0.0002994793,0.0003159537,0.000241924,0.0004103919,0.000980373,0.0005264166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006296788,"about_ca_system_score_gemma":0.0002981182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006109576,"about_ca_topic_score_gemma":0.0007753833,"domain_scores_codex":[0.999652,0.00006258412,0.0000294,0.00007087227,0.0001114535,0.00007361486],"domain_scores_gemma":[0.9998241,0.00004204511,0.00005527484,0.00001793885,0.000025423,0.00003528324],"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":[0.00005872186,0.00005381784,0.00008935826,0.0000324811,0.000008523295,0.00004618539,0.00002146958,0.0007934463,0.9970177,0.000320032,0.00004568058,0.001512564],"study_design_scores_gemma":[0.00002274509,0.0002476052,0.0002099121,0.000003265741,0.00001648502,0.0001575823,0.00001054419,0.004471316,0.9922435,0.0000539571,0.002554736,0.000008462323],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8533724,0.0009161553,0.1409586,0.0003295509,0.00005551918,0.0005074317,0.0004130097,0.0006308514,0.002816534],"genre_scores_gemma":[0.928244,0.0007691116,0.06563742,0.0001459041,0.00001323827,0.0002956115,0.0006429481,0.0001163852,0.004135311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008214352,"threshold_uncertainty_score":0.004568636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008865696954672682,"score_gpt":0.2012939879018207,"score_spread":0.192428290947148,"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."}}