{"id":"W4282940655","doi":"10.1038/s42003-022-03475-w","title":"A streamlined strain engineering workflow with genome-wide screening detects enhanced protein secretion in Komagataella phaffii","year":2022,"lang":"en","type":"article","venue":"Communications Biology","topic":"Fungal and yeast genetics research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"JST-Mirai Program; Core Research for Evolutional Science and Technology; Institute of Genetics; Ministry of Education, Culture, Sports, Science and Technology; Japan Society for the Promotion of Science; Japan Science and Technology Agency; New Energy and Industrial Technology Development Organization; Ministry of Economy, Trade and Industry; Japan Agency for Medical Research and Development","keywords":"Secretion; Gene knockout; Gene; Mutant; Computational biology; Biology; Secretory protein; Genome; Protein engineering; Metabolic engineering; Workflow; Strain (injury); Synthetic biology; Cell biology; Genetics; Computer science; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003081729,0.000137074,0.0001346705,0.0001340167,0.000207753,0.0000142124,0.0007830171,0.00008490117,0.0000416881],"category_scores_gemma":[0.00009967528,0.0001385769,0.00003864906,0.0003358733,0.0001122085,0.000003042527,0.0008479002,0.0003359704,0.000003503562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003844355,"about_ca_system_score_gemma":0.00009761265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000025871,"about_ca_topic_score_gemma":0.0002265349,"domain_scores_codex":[0.9988227,0.0002796371,0.0002011831,0.0002988804,0.00008916555,0.0003083597],"domain_scores_gemma":[0.9987926,0.00004797597,0.00006315986,0.0009706551,0.00006149024,0.00006409633],"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":[0.0001245661,0.00007870193,0.001760857,0.000008291836,0.00002890511,0.000001440354,0.00006579465,0.005820868,0.9856168,0.00008553127,0.000005804207,0.006402433],"study_design_scores_gemma":[0.007114226,0.005922074,0.03204411,0.0001485475,0.00004667206,0.00008696793,0.001388526,0.04782327,0.8002689,0.0006897757,0.1024542,0.002012725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818187,0.001558281,0.01505941,0.0004756301,0.00001980739,0.0005166278,0.00008523705,0.00002450379,0.0004417553],"genre_scores_gemma":[0.9822826,0.000136767,0.01559604,0.00006082106,0.0000330779,0.0005074601,0.001191093,0.00002398736,0.0001681051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1853479,"threshold_uncertainty_score":0.5651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723368190097015,"score_gpt":0.2646902390732336,"score_spread":0.2474565571722634,"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."}}