{"id":"W2157950340","doi":"10.1110/ps.0376003","title":"High‐yield expression of isotopically labeled peptides for use in NMR studies","year":2003,"lang":"en","type":"article","venue":"Protein Science","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institutes of Health Research; University of Alberta","funders":"Canadian Institutes of Health Research; National Institutes of Health; Fondation pour la Recherche Médicale; Heart and Stroke Foundation of Canada","keywords":"Peptide; Histidine; Chemistry; Nuclear magnetic resonance spectroscopy; Yield (engineering); Recombinant DNA; Biochemistry; Fusion protein; Amino acid; Cleavage (geology); Peptide sequence; Lysine; Mass spectrometry; Combinatorial chemistry; Chromatography; Stereochemistry; Biology; Materials science","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":[0.0005849383,0.000446996,0.0006569655,0.0002870587,0.0002596492,0.000459266,0.0003862162,0.0003709405,0.0007030847],"category_scores_gemma":[0.0004750433,0.0002627541,0.000274517,0.0004425795,0.0003657739,0.0003510326,0.0003912174,0.000733337,0.0006618917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004198144,"about_ca_system_score_gemma":0.0002692478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005264628,"about_ca_topic_score_gemma":0.000661352,"domain_scores_codex":[0.9997708,0.00005213293,0.00002144462,0.00003194012,0.00008579109,0.00003790318],"domain_scores_gemma":[0.9997837,0.00004782962,0.00003766045,0.00003774358,0.00005718269,0.00003589821],"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":[0.00002588516,0.000005762231,0.00003389682,0.00002910652,0.00000256973,0.00001798819,0.00001546154,0.00008409867,0.9990748,0.00009054381,0.00003149919,0.0005883003],"study_design_scores_gemma":[0.00001473258,0.00007076718,0.0004696724,0.000006238261,0.00001082261,0.0001849275,0.00001666146,0.0008958565,0.9941701,0.0001071068,0.00404792,0.000005328276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8714063,0.003104473,0.120133,0.0003543653,0.0001029066,0.0002516347,0.001842022,0.000431081,0.002374165],"genre_scores_gemma":[0.8289422,0.004137266,0.1539002,0.0001289041,0.00004106007,0.000282763,0.006515846,0.0003304791,0.005721282],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0007030847,"threshold_uncertainty_score":0.003093541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04382422434508315,"score_gpt":0.294598710148944,"score_spread":0.2507744858038609,"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."}}