{"id":"W2234497665","doi":"10.1002/pro.2876","title":"How mutational epistasis impairs predictability in protein evolution and design","year":2016,"lang":"en","type":"article","venue":"Protein Science","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Epistasis; Mutation; Biology; Genetics; Point mutation; Evolutionary biology; Computational biology; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001696612,0.0004128802,0.0005514201,0.0005584509,0.000463,0.001283647,0.0004366565,0.0005892543,0.001058076],"category_scores_gemma":[0.006324791,0.000305339,0.0005286922,0.0004796745,0.0009291074,0.001317061,0.001079905,0.0008053764,0.0001675695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006936014,"about_ca_system_score_gemma":0.0008635433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00139797,"about_ca_topic_score_gemma":0.001736506,"domain_scores_codex":[0.9985316,0.0005283516,0.0001214779,0.0002916869,0.0003410041,0.0001858018],"domain_scores_gemma":[0.9982058,0.0007732401,0.0004724185,0.0003022504,0.000133834,0.0001124479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006754057,0.000259176,0.08463828,0.0004788275,0.0007544776,0.001071169,0.0006671831,0.1644539,0.5713569,0.05582035,0.0006960364,0.1191283],"study_design_scores_gemma":[0.0001237876,0.002152372,0.1515015,0.0001201428,0.0008221816,0.003301793,0.0007648985,0.4638854,0.2373774,0.1250812,0.01455264,0.0003166491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559904,0.001009029,0.03920664,0.0003634181,0.0000204559,0.00002637891,0.00006478981,0.0001880093,0.00313102],"genre_scores_gemma":[0.9949544,0.0002680964,0.004480427,0.00003734574,0.000003665917,0.000009501849,0.00003180665,0.00002471333,0.0001899959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001696612,"threshold_uncertainty_score":0.008972645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021736249981421,"score_gpt":0.2388935581320025,"score_spread":0.2286761956321883,"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."}}