{"id":"W4316363245","doi":"10.1101/2023.01.13.523785","title":"Design of amyloidogenic peptide traps","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Argonne National Laboratory; National Institute of General Medical Sciences; Office of Science; National Institutes of Health; Canadian Institutes of Health Research; Defense Advanced Research Projects Agency; Natural Sciences and Engineering Research Council of Canada; Amgen; U.S. Department of Energy","keywords":"Peptide; Binding affinities; Amyloid (mycology); Chemistry; Affinities; Biophysics; Fibril; Rational design; Amyloid fibril; Transthyretin; Hydrogen bond; In vitro; Biochemistry; Nanotechnology; Biology; Materials science; Amyloid β; Molecule","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":[0.0002680485,0.0003264146,0.0002893752,0.0002666586,0.0001983457,0.0004492276,0.0006407715,0.0004896739,0.0009459245],"category_scores_gemma":[0.0002234394,0.0002535789,0.0001780472,0.0001302399,0.0002073915,0.0003126742,0.0004145907,0.0004872358,0.0004617038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780318,"about_ca_system_score_gemma":0.0002214546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001243691,"about_ca_topic_score_gemma":0.0002106359,"domain_scores_codex":[0.9998337,0.00002078948,0.0000112426,0.00004076559,0.00005348254,0.00003996815],"domain_scores_gemma":[0.9998473,0.00002044583,0.00004268207,0.00001156914,0.00003400388,0.00004399522],"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":[0.0001357044,0.000203562,0.0003986703,0.0001346031,0.00002288117,0.0001379539,0.00004263549,0.005810598,0.979342,0.003402149,0.0004158368,0.009953394],"study_design_scores_gemma":[0.0001398814,0.0008692975,0.0004288346,0.00001757338,0.00002047175,0.0002189993,0.00003164618,0.04282648,0.9451448,0.0009905469,0.009288712,0.00002266379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8358241,0.002420646,0.1533183,0.0002782237,0.0001408663,0.0005919965,0.000411318,0.0007195917,0.00629491],"genre_scores_gemma":[0.9301347,0.0005742644,0.06547861,0.0001475614,0.00001432613,0.0003759676,0.0002388834,0.00004016736,0.002995551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009459245,"threshold_uncertainty_score":0.003164411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668188331640247,"score_gpt":0.2230544384780212,"score_spread":0.2063725551616188,"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."}}