{"id":"W3193986322","doi":"10.1101/2021.08.21.457196","title":"AlphaFold2 transmembrane protein structure prediction shines","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Canadian Institutes of Health Research; University of Bern; Semmelweis Egyetem; Cystic Fibrosis Foundation","keywords":"Transmembrane protein; Computational biology; Protein structure; Computer science; Biological system; Protein structure prediction; Artificial neural network; Function (biology); Transmembrane domain; Structural bioinformatics; Biophysics; Chemistry; Artificial intelligence; Biology; Biochemistry; Genetics; 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":[0.0008176316,0.0008489993,0.0005223923,0.0006872441,0.0002866559,0.0005925552,0.0006419274,0.0007185497,0.003858797],"category_scores_gemma":[0.00109338,0.0002260655,0.0005572658,0.0004468509,0.0001697699,0.0005842887,0.0003697246,0.0005149406,0.001427845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004567959,"about_ca_system_score_gemma":0.000555968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174731,"about_ca_topic_score_gemma":0.001472955,"domain_scores_codex":[0.9997366,0.00006100645,0.00001501901,0.00007813067,0.0000818665,0.00002739913],"domain_scores_gemma":[0.9995783,0.0001073897,0.00003738536,0.00009365733,0.0001231947,0.0000600879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002695441,0.0005199253,0.0412631,0.0007057099,0.0004805167,0.0008723048,0.0001560017,0.5580308,0.1225738,0.007402304,0.09521357,0.1700865],"study_design_scores_gemma":[0.00003344087,0.00005620399,0.001660923,0.000009391876,0.000009229768,0.00009518725,0.00001200625,0.9736589,0.02034157,0.001233452,0.002880861,0.000008798786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7729607,0.001416023,0.1661052,0.001055377,0.0004476132,0.0001008417,0.009950252,0.03953914,0.008424797],"genre_scores_gemma":[0.8700652,0.0002839349,0.1068036,0.0001211183,0.00004425333,0.00005972038,0.01689573,0.001281883,0.004444535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003858797,"threshold_uncertainty_score":0.012909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005289297564305528,"score_gpt":0.197925888853701,"score_spread":0.1926365912893955,"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."}}