{"id":"W4390572302","doi":"10.1007/s10858-023-00431-6","title":"Beyond slow two-state protein conformational exchange using CEST: applications to three-state protein interconversion on the millisecond timescale","year":2024,"lang":"en","type":"article","venue":"Journal of Biomolecular NMR","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Tata Institute of Fundamental Research","keywords":"Chemistry; Millisecond; Protein folding; Protein dynamics; Conformational isomerism; Nuclear magnetic resonance spectroscopy; Chemical shift; Chemical physics; Molecular dynamics; Crystallography; Molecule; Computational chemistry; Stereochemistry; Physical chemistry; Physics","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":[0.001024705,0.0005225235,0.0005985735,0.000232285,0.0003505643,0.0009523532,0.0005862712,0.0008082954,0.001235464],"category_scores_gemma":[0.0009237838,0.0002497618,0.0003268512,0.0004203335,0.0009087806,0.001348965,0.0008400413,0.001625177,0.0003325716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000542852,"about_ca_system_score_gemma":0.0004634563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004937436,"about_ca_topic_score_gemma":0.0007047692,"domain_scores_codex":[0.9998485,0.000032254,0.00000770114,0.00003900889,0.0000448892,0.00002752209],"domain_scores_gemma":[0.9993236,0.0004051836,0.00004428144,0.0001054939,0.00006562466,0.00005582053],"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":[0.0001894821,0.00004224644,0.0003831178,0.0002078719,0.000012821,0.0001098474,0.0001719893,0.001019302,0.9637541,0.007016563,0.0006059888,0.02648669],"study_design_scores_gemma":[0.00003914129,0.0001966541,0.001110502,0.00002837102,0.00002775973,0.0003292098,0.0000979573,0.02857017,0.9463803,0.007057366,0.01611759,0.00004500523],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5326678,0.0164754,0.4326763,0.00429015,0.0005360504,0.0001614873,0.0003625902,0.001539277,0.01129099],"genre_scores_gemma":[0.8851262,0.01077219,0.09741422,0.0006609715,0.0001345736,0.0001265494,0.0002227326,0.0002759254,0.005266561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001235464,"threshold_uncertainty_score":0.005419254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657816850844323,"score_gpt":0.2870411588284242,"score_spread":0.2704629903199809,"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."}}