{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001563856,0.0005195764,0.0005164426,0.001116498,0.0002698977,0.0005596517,0.0008305451,0.0005522604,0.0009441111],"category_scores_gemma":[0.002095053,0.0001609204,0.0005673289,0.0007985526,0.0006327629,0.0002994833,0.000501849,0.0006818986,0.00006791134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206977,"about_ca_system_score_gemma":0.0004246265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001884848,"about_ca_topic_score_gemma":0.001405566,"domain_scores_codex":[0.9993526,0.0002648699,0.00002389453,0.0001906069,0.0001236877,0.00004433475],"domain_scores_gemma":[0.9990958,0.0006884263,0.00008457925,0.00004932716,0.00004310724,0.00003882349],"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":[0.0005019764,0.0003223615,0.01903627,0.0002176783,0.0003360548,0.0001591252,0.0002304451,0.07089036,0.856212,0.01148279,0.0002658335,0.04034519],"study_design_scores_gemma":[0.0000659402,0.00110158,0.07547267,0.00002053253,0.0002401059,0.0003764966,0.0002101653,0.8098379,0.1038219,0.006906685,0.001827696,0.0001183262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9133423,0.0001544894,0.08439026,0.0000947173,0.00001159115,0.0000455222,0.0006163625,0.0002651768,0.001079617],"genre_scores_gemma":[0.9444328,0.0001268036,0.05402637,0.00007713984,0.000007865346,0.0001325159,0.000635822,0.00008502881,0.0004755883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001884848,"threshold_uncertainty_score":0.008757293,"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."}}