{"id":"W4380447188","doi":"10.1101/2023.06.13.544731","title":"Maximizing Heterologous Expression of Engineered Type I Polyketide Synthases: Investigating Codon Optimization Strategies","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Office of Energy Efficiency; Bioenergy Technologies Office; Biological and Environmental Research; Philomathia Foundation; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; Office of Science; National Science Foundation","keywords":"Codon usage bias; Polyketide; Heterologous; Computational biology; Heterologous expression; Start codon; Biology; Corynebacterium glutamicum; Synthetic biology; Pseudomonas putida; Stop codon; Escherichia coli; Gene; Genetics; Biosynthesis; Genome; Messenger RNA; Recombinant DNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006223234,0.0006477494,0.0005142792,0.0003456596,0.0001843358,0.0009428356,0.000322348,0.0003568072,0.000514789],"category_scores_gemma":[0.0007816881,0.0001806768,0.0003374934,0.0006093125,0.0002576426,0.0004983589,0.000376901,0.0008436964,0.0003890278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004903589,"about_ca_system_score_gemma":0.0003189721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004962563,"about_ca_topic_score_gemma":0.0008066195,"domain_scores_codex":[0.9995739,0.00009041348,0.00005516907,0.0000864044,0.000133405,0.00006069559],"domain_scores_gemma":[0.9997224,0.00009153446,0.0000797877,0.00003264191,0.00004771143,0.00002602451],"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":[0.00009854051,0.00006108858,0.0004888506,0.000106831,0.00001278886,0.00005753543,0.00003067605,0.001183891,0.9935906,0.0001821236,0.00006374352,0.004123391],"study_design_scores_gemma":[0.000005705841,0.000109842,0.0005442716,0.000009402245,0.0000183586,0.00006206104,0.00002447452,0.00272456,0.9952071,0.00008535136,0.001198831,0.000009957314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771149,0.002008134,0.01870699,0.0001572176,0.00003784592,0.00005926297,0.0005023631,0.000191481,0.001221751],"genre_scores_gemma":[0.9574374,0.002242914,0.0381551,0.0000698588,0.00001213003,0.00005240692,0.0007858502,0.0002262079,0.001018174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009428356,"threshold_uncertainty_score":0.003557801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714414854459402,"score_gpt":0.2391148718312666,"score_spread":0.2119707232866726,"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."}}