{"id":"W3202485137","doi":"10.1101/2021.09.27.461910","title":"AlphaFold2: A role for disordered protein prediction?","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":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Compute Canada","keywords":"Metric (unit); Identification (biology); Intrinsically disordered proteins; Similarity (geometry); Statistical physics; Psychology; Computer science; Artificial intelligence; Physics; Biology; Nuclear magnetic resonance; Ecology; Engineering","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":[0.008085779,0.00152637,0.001429716,0.001618964,0.0005426764,0.002348635,0.001195067,0.001087323,0.00354838],"category_scores_gemma":[0.01320321,0.0003613307,0.0004206326,0.001171921,0.0009005544,0.002310483,0.001363533,0.001391559,0.001560967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00060294,"about_ca_system_score_gemma":0.0009707045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008858365,"about_ca_topic_score_gemma":0.001029254,"domain_scores_codex":[0.9977819,0.001101761,0.00008722865,0.0003867772,0.0005321992,0.0001102081],"domain_scores_gemma":[0.9924349,0.004005516,0.0005458001,0.001118154,0.001009241,0.0008863503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008016858,0.001074187,0.2084222,0.001488325,0.0007953345,0.0009151628,0.0004641492,0.1871615,0.09195252,0.04658156,0.09847025,0.3546579],"study_design_scores_gemma":[0.0001076821,0.0003617218,0.004275219,0.0001001128,0.00003344527,0.0003991625,0.00007637962,0.9351716,0.03371742,0.01641491,0.00928893,0.00005334659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5581977,0.006221283,0.3629735,0.007404487,0.001128531,0.0001714844,0.008024856,0.04326523,0.01261299],"genre_scores_gemma":[0.8089352,0.0008496673,0.1788529,0.0007571077,0.0002908995,0.00009596635,0.005946845,0.001607646,0.002663629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008085779,"threshold_uncertainty_score":0.04276216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005546162010990512,"score_gpt":0.2066445532659017,"score_spread":0.2010983912549112,"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."}}