{"id":"W4409313764","doi":"10.1101/2025.04.07.643449","title":"Engineering IspH for Enhanced Terpenoid Yield: Computational and Molecular Dynamics Studies","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant biochemistry and biosynthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Opal-Rt Technologies (Canada)","funders":"","keywords":"Terpenoid; Molecular dynamics; Yield (engineering); Dynamics (music); Chemistry; Biological system; Biochemical engineering; Computer science; Computational chemistry; Physics; Engineering; Biology; Stereochemistry; Thermodynamics","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":[0.0003183882,0.0005990589,0.0008431142,0.0004124891,0.0006038265,0.0006973841,0.0005716055,0.0005881473,0.003015629],"category_scores_gemma":[0.0004792301,0.0002544903,0.0004973363,0.000456902,0.0002837105,0.0004382714,0.0003130253,0.0007730817,0.0002564158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008451005,"about_ca_system_score_gemma":0.001098935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130835,"about_ca_topic_score_gemma":0.01061975,"domain_scores_codex":[0.9999363,0.0000119967,0.000002883173,0.0000112652,0.00001657786,0.00002096132],"domain_scores_gemma":[0.9998212,0.00008571421,0.00001706734,0.0000123703,0.00003407918,0.00002960597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002623509,0.0003328639,0.003708304,0.0002538589,0.0001020905,0.0003705662,0.0000918472,0.9669845,0.01183693,0.005474529,0.001403072,0.009179113],"study_design_scores_gemma":[0.0000482579,0.00006853965,0.0004196747,0.000006779061,0.00001451651,0.00001282106,0.00003288252,0.9969933,0.001513654,0.000450028,0.0004326621,0.000006894516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829924,0.0004634259,0.006913942,0.0003484176,0.00003911296,0.00004391679,0.0007917003,0.0002495217,0.008157576],"genre_scores_gemma":[0.9844484,0.0004783616,0.0128634,0.000050027,0.00001319693,0.00008240966,0.0008321764,0.000066856,0.001165187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01130835,"threshold_uncertainty_score":0.02248502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008740897580490316,"score_gpt":0.2228011541882982,"score_spread":0.2140602566078079,"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."}}