{"id":"W4411707447","doi":"10.1038/s41587-026-03116-1","title":"Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery","year":2025,"lang":"en","type":"preprint","venue":"Nature Biotechnology","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Zero (linguistics); Computational biology; Computer science; Biology; Philosophy","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":[0.001479817,0.000677857,0.0006039525,0.0004818927,0.0005639473,0.00127731,0.001092297,0.0008169019,0.002863363],"category_scores_gemma":[0.001627174,0.0005528292,0.0004754608,0.0006139606,0.0006534461,0.001220008,0.001550925,0.00147752,0.001414326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893052,"about_ca_system_score_gemma":0.0008695128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004917908,"about_ca_topic_score_gemma":0.0009439911,"domain_scores_codex":[0.9991754,0.0001288473,0.00003494047,0.0002062517,0.0003666246,0.00008795786],"domain_scores_gemma":[0.9993429,0.000188973,0.00004497737,0.0002424012,0.0001179234,0.00006277268],"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":[0.001427885,0.0001580868,0.001275279,0.0006200183,0.0002317242,0.0004190487,0.0001909417,0.004534966,0.8347164,0.03789238,0.004827545,0.1137057],"study_design_scores_gemma":[0.00009936308,0.000211609,0.00154454,0.00003429082,0.00007691881,0.0006374693,0.00004077234,0.07404564,0.8658962,0.03353029,0.02380876,0.00007417704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1918939,0.00119493,0.7866588,0.0003505203,0.0004322945,0.000182506,0.001870004,0.005440847,0.01197621],"genre_scores_gemma":[0.4381841,0.0009122679,0.5449703,0.0002450908,0.0001077489,0.0002048389,0.00434769,0.001104889,0.009923022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002863363,"threshold_uncertainty_score":0.009578943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368220203031667,"score_gpt":0.2864098803748288,"score_spread":0.2727276783445122,"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."}}