{"id":"W4205172550","doi":"10.1007/s00018-021-04112-1","title":"Ins and outs of AlphaFold2 transmembrane protein structure predictions","year":2022,"lang":"en","type":"article","venue":"Cellular and Molecular Life Sciences","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Semmelweis Egyetem; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada Foundation for Innovation; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Cystic Fibrosis Canada; Cystic Fibrosis Foundation; University of Bern; Canada Research Chairs; DeepMind","keywords":"Transmembrane protein; Computational biology; Membrane protein; Protein structure; Computer science; Biological system; Protein structure prediction; Transmembrane domain; Reliability (semiconductor); Biophysics; Chemistry; Artificial intelligence; Biology; Biochemistry; Physics; Gene; Membrane","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000280427,0.0001174641,0.0001323482,0.0000633883,0.0003327304,0.0000179063,0.0001886643,0.00005874118,0.00006798332],"category_scores_gemma":[0.00003849595,0.0001076185,0.00005058676,0.0001419311,0.0002693024,0.000005832979,0.0001365315,0.00007016147,2.309507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002601339,"about_ca_system_score_gemma":0.00009137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001652662,"about_ca_topic_score_gemma":0.000003173679,"domain_scores_codex":[0.9989612,0.0001197972,0.0001570027,0.0003517976,0.0002392686,0.0001709061],"domain_scores_gemma":[0.9996391,0.000005615858,0.00006664939,0.0001652012,0.00002250104,0.0001009505],"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":[0.00001968224,0.00002184375,0.00006918424,0.00002529046,0.00002077131,0.000003449107,0.00009445291,0.0001251766,0.9977121,0.001097354,0.00004716536,0.0007635802],"study_design_scores_gemma":[0.0002607705,0.0005221179,0.00006425329,0.000007850967,0.00002217058,0.00001577829,0.0002798277,0.0001547,0.9909494,0.0007723761,0.006810462,0.0001403129],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865725,0.005105608,0.007086159,0.0002768836,0.00008015835,0.0003028824,0.0000575732,0.000008180114,0.0005100448],"genre_scores_gemma":[0.9984658,0.00006893941,0.0009969909,0.0001807438,0.00002932611,0.00003549136,0.00001881475,0.00001005543,0.0001937827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01189335,"threshold_uncertainty_score":0.4388556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008197626970576764,"score_gpt":0.2146005367709967,"score_spread":0.2064029098004199,"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."}}