{"id":"W4410484052","doi":"10.1016/j.ijbiomac.2025.144379","title":"Point mutations enhance catalytic efficiency of Geobacillus stearothermophilus α-glucosidase: A biochemical characterization study","year":2025,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Karadeniz Teknik Üniversitesi","keywords":"Geobacillus stearothermophilus; Characterization (materials science); Point mutation; Chemistry; Computational biology; Biochemistry; Thermophile; Biology; Mutation; Enzyme; Materials science; Nanotechnology; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001242829,0.0003665361,0.0001954107,0.0001633715,0.00008762132,0.0002085756,0.0002008404,0.0002554432,0.0009054996],"category_scores_gemma":[0.0002204058,0.0001440814,0.000276274,0.000222918,0.0001795126,0.0001700356,0.0001531055,0.0003612359,0.0003181002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716653,"about_ca_system_score_gemma":0.0001451003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009002832,"about_ca_topic_score_gemma":0.0007695408,"domain_scores_codex":[0.9998324,0.00003581163,0.00003085725,0.00002292574,0.00004873416,0.00002925945],"domain_scores_gemma":[0.9998574,0.00004039773,0.00003532475,0.00001940713,0.00002379548,0.00002359203],"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":[0.00006779212,0.00003198007,0.0001741553,0.000008617551,0.00000555603,0.00003427631,0.000008637743,0.00007322104,0.999069,0.00003583859,0.000009578835,0.0004813992],"study_design_scores_gemma":[0.00000570276,0.0001214291,0.002218694,0.000002081612,0.00001433294,0.0001302722,0.00001969631,0.0006245542,0.9965556,0.00001232676,0.0002925223,0.000002820536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983194,0.0001428554,0.001059343,0.00002895,0.000007349061,0.00001021717,0.0001017076,0.00002261996,0.00030745],"genre_scores_gemma":[0.9985853,0.0001114552,0.0005568787,0.00001263899,0.000002029402,0.000005087427,0.0001895468,0.00001266318,0.0005243256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009054996,"threshold_uncertainty_score":0.003029227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008412133060606342,"score_gpt":0.2820337455051871,"score_spread":0.2736216124445808,"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."}}