{"id":"W2950544665","doi":"10.1371/journal.pone.0196135","title":"The response to selection in Glycoside Hydrolase Family 13 structures: A comparative quantitative genetics approach","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Protein Data Bank (RCSB PDB); Glycoside hydrolase; Hydrolase; Selection (genetic algorithm); Biology; Computational biology; Phylogenetic tree; Hydrolysis; Genetics; Protein structure; Sequence alignment; In silico; Biochemistry; Peptide sequence; Enzyme; Gene; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002263211,0.0001053213,0.0001056324,0.00005371989,0.0001469288,0.00003469184,0.0001110677,0.00007108434,0.000004209552],"category_scores_gemma":[0.000168766,0.00008781192,0.00001999669,0.0002229023,0.00008326748,0.000004530793,0.00004339698,0.00008441229,0.00002110203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002357989,"about_ca_system_score_gemma":0.00004960537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009063428,"about_ca_topic_score_gemma":0.000106368,"domain_scores_codex":[0.9990312,0.0002207717,0.0001641664,0.0002845396,0.0001389078,0.0001604452],"domain_scores_gemma":[0.9995139,0.00002173895,0.00006076583,0.0001681568,0.0001852701,0.00005015547],"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":[0.001887922,0.0001929936,0.0006170074,0.000005544056,0.00005336135,1.517502e-7,0.0008398372,0.00008820249,0.995895,0.00006324227,0.0002839578,0.00007273085],"study_design_scores_gemma":[0.0003511443,0.0008480814,0.02278071,0.00001184248,0.00001583382,0.000001911519,0.0005537079,0.001889619,0.9710745,0.00006551809,0.002268931,0.0001382129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968213,0.0001514195,0.002020306,0.0002899202,0.00003926624,0.0003404664,0.000009119712,0.00001276901,0.0003153969],"genre_scores_gemma":[0.9938924,0.00006286189,0.004924714,0.0003186338,0.0001963253,0.000054423,0.00003947302,0.0000129774,0.0004981815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02482056,"threshold_uncertainty_score":0.3580866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05679152299477499,"score_gpt":0.2861759542640013,"score_spread":0.2293844312692263,"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."}}