{"id":"W4417307099","doi":"10.1021/acs.jafc.5c11512","title":"Genome Mining Guided Discovery of Macrocyclic Sesterterpenoids with Anti-MRSA and Anti-Neuroinflammatory Activities","year":2025,"lang":"en","type":"article","venue":"Journal of Agricultural and Food Chemistry","topic":"Plant biochemistry and biosynthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Alpha Technologies (Canada)","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; State Key Laboratory of Bioreactor Engineering; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Terpenoid; Gene cluster; Genome; Terpene; Bifunctional; Heterologous expression","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":[0.0001703178,0.0004578834,0.0006887413,0.001100135,0.0001949365,0.0005683564,0.0002815207,0.0002870831,0.001068465],"category_scores_gemma":[0.0002649915,0.0001451572,0.0007297394,0.001199564,0.0001195423,0.0002445404,0.0002953294,0.0003687633,0.0003795231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002566359,"about_ca_system_score_gemma":0.0006376368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009100135,"about_ca_topic_score_gemma":0.002272078,"domain_scores_codex":[0.9999154,0.00001001075,0.000006742006,0.00002806832,0.00002343342,0.00001633276],"domain_scores_gemma":[0.9999087,0.00001815721,0.00003083575,0.000007228828,0.00002085343,0.00001417144],"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":[0.0004683704,0.0001529354,0.006160331,0.0005532986,0.00008139484,0.0006684219,0.00007557782,0.002093553,0.9498896,0.0003787194,0.000578175,0.03889962],"study_design_scores_gemma":[0.0002059359,0.002432601,0.09098784,0.0001570639,0.001190012,0.003977673,0.0007704712,0.04240822,0.805649,0.001572616,0.05055518,0.00009331227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515835,0.005973008,0.02510107,0.0004545271,0.00004590012,0.0002995822,0.01255893,0.0007110366,0.003272472],"genre_scores_gemma":[0.909359,0.004880625,0.05770136,0.0001901319,0.00002360334,0.0001460336,0.02427458,0.0001212029,0.00330341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001100135,"threshold_uncertainty_score":0.003574371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004946402938147143,"score_gpt":0.1889901400823177,"score_spread":0.1840437371441706,"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."}}