{"id":"W2566166953","doi":"10.1021/acs.analchem.6b03625","title":"The “PepSAVI-MS” Pipeline for Natural Product Bioactive Peptide Discovery","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Biochemical and Structural Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Higher Education Commission, Pakistan; Ministry of Science and Technology, Pakistan; United States Agency for International Development; National Institute of General Medical Sciences; U.S. Department of State","keywords":"Chemistry; Natural product; Antimicrobial; Peptide; Antifungal; Drug discovery; Computational biology; Antibiotics; Antibacterial activity; Identification (biology); Biochemistry; Bacteria; Microbiology; Biology; Botany","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002275394,0.001183718,0.000681626,0.00146379,0.0004075457,0.001468973,0.0008887432,0.0007311734,0.006242019],"category_scores_gemma":[0.00170531,0.0005191445,0.0008818048,0.0006834558,0.0004745484,0.001528803,0.001611462,0.001623921,0.003416031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004261758,"about_ca_system_score_gemma":0.0008413214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002979718,"about_ca_topic_score_gemma":0.0003963711,"domain_scores_codex":[0.9993058,0.0001158527,0.00003819106,0.0001753843,0.0003021924,0.00006248952],"domain_scores_gemma":[0.9992616,0.0002186304,0.0001513115,0.000100886,0.000165258,0.0001023844],"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":[0.001853224,0.0001722602,0.003353458,0.000678781,0.0003587132,0.0004535163,0.0001480672,0.002470121,0.8284035,0.007380547,0.01307957,0.1416483],"study_design_scores_gemma":[0.000238501,0.001060352,0.003955838,0.00009255527,0.0001075841,0.001368584,0.00006726511,0.05477648,0.8306357,0.00700337,0.1005352,0.0001586049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1271258,0.008380431,0.7795719,0.001982205,0.0006050015,0.001115809,0.01779054,0.03889742,0.02453088],"genre_scores_gemma":[0.3223577,0.00451897,0.6372613,0.001469375,0.0003837463,0.0009479995,0.02054013,0.002319799,0.01020095],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006242019,"threshold_uncertainty_score":0.02088159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006638980382419782,"score_gpt":0.2391754693789107,"score_spread":0.232536488996491,"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."}}