{"id":"W4206681893","doi":"10.26434/chemrxiv.11749854.v2","title":"Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Enzyme Catalysis and Immobilization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Knut och Alice Wallenbergs Stiftelse; Ministerio de Ciencia, Innovación y Universidades","keywords":"Directed evolution; Active site; Protein engineering; Folding (DSP implementation); Computational biology; Sequence space; Stability (learning theory); Directed Molecular Evolution; Protein stability; Ranking (information retrieval); Protein folding; Chemistry; Computer science; Protein design; Sequence (biology); Enzyme; Protein structure; Biology; Mathematics; Machine learning; Biochemistry; Gene; Mutant; Engineering","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":[0.001428025,0.0009320968,0.001072399,0.0005725705,0.0003031118,0.001264292,0.001091674,0.0005644237,0.002356905],"category_scores_gemma":[0.002437316,0.0004654615,0.0006070342,0.0004750657,0.0004073983,0.0009018393,0.0008957509,0.001198455,0.001321145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006428068,"about_ca_system_score_gemma":0.0008629958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004470254,"about_ca_topic_score_gemma":0.001038591,"domain_scores_codex":[0.9994855,0.00009966574,0.00004412862,0.0001179633,0.0001900662,0.00006273891],"domain_scores_gemma":[0.9993093,0.0003219619,0.00008075521,0.0001352742,0.0001118139,0.00004078526],"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":[0.0004556841,0.000377096,0.004306321,0.0005133689,0.0001110052,0.0003255534,0.0000920756,0.0747869,0.8248798,0.007624547,0.001171147,0.08535661],"study_design_scores_gemma":[0.00008288899,0.0005850011,0.001504084,0.00002990057,0.00008989554,0.0003930914,0.00003589362,0.2943342,0.6920829,0.002334197,0.008490083,0.00003782487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5923034,0.001077625,0.3935885,0.0004089372,0.00007305924,0.0002131569,0.0008133645,0.004025859,0.007496028],"genre_scores_gemma":[0.7434179,0.0009442428,0.2512998,0.0001362587,0.00001450053,0.000210935,0.001261226,0.0004585013,0.002256658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002356905,"threshold_uncertainty_score":0.007884681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469511157357157,"score_gpt":0.2761219547140096,"score_spread":0.2514268431404381,"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."}}