{"id":"W3158876629","doi":"10.1093/nargab/lqab027","title":"On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction","year":2021,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Instituto de Salud Carlos III; Ministerio de Economía y Competitividad; Generalitat de Catalunya","keywords":"Protein secondary structure; Protein folding; Sequence (biology); Protein structure prediction; Protein structure; Structural motif; Alphabet; Computational biology; Protein sequencing; Amino acid; Chemistry; Biological system; Peptide sequence; Crystallography; Biology; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001075253,0.0001643697,0.0002830132,0.00006229619,0.00009515691,0.00003583733,0.00009767715,0.0001397505,0.000004006395],"category_scores_gemma":[0.00005010813,0.0001008119,0.00008443888,0.0002031658,0.000100914,0.00001213945,0.00005273925,0.0001029673,3.969766e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001219392,"about_ca_system_score_gemma":0.0001429076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006572624,"about_ca_topic_score_gemma":0.00005846303,"domain_scores_codex":[0.9992337,0.00001450391,0.0003175089,0.0001638987,0.0001241142,0.0001462363],"domain_scores_gemma":[0.9991338,0.0000308739,0.0002198536,0.0003607143,0.0002180072,0.00003674621],"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":[0.001884175,0.00008852341,0.00304068,0.0004943596,0.002292244,0.000001647764,0.0007743719,0.01028584,0.9771647,0.001077081,0.0000968363,0.00279948],"study_design_scores_gemma":[0.001910944,0.002913116,0.002126418,0.00008005489,0.0007003131,0.00001745115,0.0005546802,0.1526633,0.8377107,0.000260654,0.0007439028,0.000318491],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885662,0.00009774276,0.009825756,0.00004300839,0.00002151369,0.000534096,0.0008704442,0.000004740188,0.00003653233],"genre_scores_gemma":[0.9753551,0.00005035595,0.02380625,0.00007565274,0.00002159719,0.00001751972,0.0006093847,0.00001452912,0.0000495902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1423775,"threshold_uncertainty_score":0.411099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008394857317764562,"score_gpt":0.1939350341460129,"score_spread":0.1855401768282484,"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."}}