{"id":"W2893054076","doi":"10.1016/j.str.2019.01.008","title":"Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1","year":2019,"lang":"en","type":"article","venue":"Structure","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Office of Research Infrastructure Programs, National Institutes of Health; National Center for Research Resources; Health Effects Institute; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Office of Science; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Argonne National Laboratory; U.S. Department of Energy; National Institutes of Health; National Science Foundation","keywords":"Motif (music); Sequence motif; Computational biology; Sequence (biology); Chemistry; Structural motif; Stereochemistry; Biology; Biochemistry; Physics; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008101301,0.000176066,0.0001727655,0.00004231351,0.00006624989,0.0000362992,0.0001053314,0.0001641834,0.00005574359],"category_scores_gemma":[0.00008018705,0.0001467643,0.00001861329,0.0000420486,0.0001391774,0.00001623722,0.0001020732,0.0001502373,0.000001360431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008439745,"about_ca_system_score_gemma":0.00004556592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002350993,"about_ca_topic_score_gemma":0.00000500291,"domain_scores_codex":[0.9991887,0.00005177176,0.0001930894,0.0002320558,0.0001436219,0.0001907114],"domain_scores_gemma":[0.9994744,0.00003020163,0.0001375221,0.00024164,0.00005522107,0.00006101722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003335977,0.000003358444,0.298282,0.0004064156,0.00005788295,0.000001123939,0.0003498646,0.003736784,0.6950076,0.0001126342,0.0004302051,0.001578806],"study_design_scores_gemma":[0.001767352,0.001005293,0.5181332,0.0001377497,0.0001203719,0.0003707231,0.0005452438,0.06193208,0.4114916,0.002700758,0.001043167,0.0007524453],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941208,0.0002052947,0.004558351,0.00001505649,0.0001388752,0.0002710719,0.0006060515,0.00001217543,0.00007235564],"genre_scores_gemma":[0.9791217,0.00009889685,0.02019543,0.0000505438,0.00004108573,0.000001509837,0.0002775302,0.00001559238,0.0001976519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.283516,"threshold_uncertainty_score":0.5984876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00801524920236433,"score_gpt":0.2272791201790436,"score_spread":0.2192638709766793,"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."}}