{"id":"W4385351582","doi":"10.1016/j.chom.2023.07.001","title":"Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning","year":2023,"lang":"en","type":"article","venue":"Cell Host & Microbe","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":145,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Perelman School of Medicine, University of Pennsylvania; Defense Threat Reduction Agency; University of Pennsylvania; Beef Cattle Research Council; ACE Foundation; Innovative Research Group Project of the National Natural Science Foundation of China; University of Texas at Austin; Brain and Behavior Research Foundation; National Institute of General Medical Sciences; United Therapeutics Corporation; National Institutes of Health; Procter and Gamble; Alfred P. Sloan Foundation","keywords":"Biology; Antimicrobial; Proteome; Protease; Antimicrobial peptides; Extant taxon; Drug discovery; Peptide; Computational biology; Biochemistry; Microbiology; Evolutionary biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003363517,0.0002811785,0.0003997936,0.0001930705,0.0002856576,0.00003092448,0.0002540291,0.0002675658,0.0003525493],"category_scores_gemma":[0.00004149645,0.0002846073,0.0002105621,0.0003597167,0.000284324,0.0001093996,0.0002074924,0.0004319246,0.0006907902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006006281,"about_ca_system_score_gemma":0.00007348492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003649403,"about_ca_topic_score_gemma":0.00001178581,"domain_scores_codex":[0.998319,0.0001777646,0.0003778501,0.0004044651,0.0000404785,0.0006804684],"domain_scores_gemma":[0.9993172,0.00008313881,0.0002411028,0.0002408476,0.00008202145,0.00003567615],"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":[0.00008594641,0.000148923,0.001955438,0.00009918764,0.00006345717,0.00001224972,0.0004139091,0.0002268094,0.9349517,0.00001813794,0.06167268,0.0003515608],"study_design_scores_gemma":[0.0008737554,0.0001395706,0.0007101331,0.00005448395,0.00004790367,0.00004128686,0.0003074866,0.00003579685,0.9145067,0.000005904629,0.08300854,0.0002684515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948854,0.002123074,0.000502306,0.0001487274,0.0004033834,0.0002119917,0.0001489288,0.0002099841,0.001366217],"genre_scores_gemma":[0.9881397,0.0004700057,0.0001357407,0.0003570491,0.00002659748,0.00000723956,0.0005640721,0.00005260987,0.01024701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02133586,"threshold_uncertainty_score":0.9999606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005779273999038274,"score_gpt":0.2044380794193067,"score_spread":0.1986588054202684,"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."}}