{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515883,0.0001624335,0.0001331503,0.00004619654,0.00003719697,0.00002289874,0.00006710312,0.0001077885,0.000001776232],"category_scores_gemma":[0.0001309311,0.0001402389,0.00003101023,0.00008384166,0.00003953293,0.000008377586,0.00003515253,0.0001247133,2.562169e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002771662,"about_ca_system_score_gemma":0.0000408919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007108034,"about_ca_topic_score_gemma":0.000001454052,"domain_scores_codex":[0.9992976,0.00006242748,0.000160617,0.000237258,0.00009001007,0.0001520922],"domain_scores_gemma":[0.9996474,0.00002732979,0.00006354263,0.00009175359,0.00009137877,0.00007853624],"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":[0.0001221295,0.0000172365,0.00005607372,0.00004204779,0.0000268458,2.283819e-7,0.00007131453,0.06922308,0.9300867,0.00003618173,0.000008964519,0.0003091738],"study_design_scores_gemma":[0.0004304479,0.0002117383,0.0002894104,0.00003912548,0.0000149189,0.000009504989,0.00000803314,0.1789722,0.8196647,0.00003127771,0.0002005411,0.0001281598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4087089,0.0001596477,0.5906674,0.00001758385,0.00006042318,0.0003685287,0.000002953173,0.00001156902,0.000003015237],"genre_scores_gemma":[0.9552268,0.000008517864,0.044556,0.00001513499,0.0001056039,0.0000454631,0.000008737615,0.00002119434,0.00001248892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.546518,"threshold_uncertainty_score":0.5718778,"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."}}