{"id":"W2444601377","doi":"10.1021/acs.jpcb.5b05230","title":"Cysteine-Specific Labeling of Proteins with a Nitroxide Biradical for Dynamic Nuclear Polarization NMR","year":2015,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences","keywords":"Site-directed spin labeling; Chemistry; Nitroxide mediated radical polymerization; Solid-state nuclear magnetic resonance; Cysteine; Nuclear magnetic resonance spectroscopy; Spins; Electron paramagnetic resonance; CIDNP; Polarization (electrochemistry); Nuclear magnetic resonance; Membrane; Stereochemistry; Organic chemistry; Physical chemistry; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001353651,0.0001184704,0.00022276,0.00001120578,0.00005925816,0.00001066155,0.0003072796,0.00005778054,0.00001147114],"category_scores_gemma":[0.00005435932,0.00007585973,0.00009277034,0.000120928,0.0001231415,0.00007601395,0.00003220308,0.0002755514,9.61722e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007324078,"about_ca_system_score_gemma":0.00005999093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002404759,"about_ca_topic_score_gemma":1.891966e-7,"domain_scores_codex":[0.9992109,0.000008135248,0.0002842913,0.00009291734,0.0002646688,0.0001390915],"domain_scores_gemma":[0.9988626,0.00008201115,0.000423084,0.0002402983,0.0002879801,0.0001040792],"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":[0.0002616349,0.0001697612,0.000007703261,0.00009116849,0.00002705121,0.000001023361,0.0001458892,0.000997473,0.9973881,0.0003595533,0.00008293561,0.0004676981],"study_design_scores_gemma":[0.001106746,0.000223214,0.000003800991,0.0002063706,0.00009497009,0.0000867432,0.0004556206,0.01081004,0.9776596,0.004405628,0.004789508,0.0001577207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649479,0.0001011475,0.03378541,0.0003668078,0.000002611336,0.0001246844,0.0000241064,0.00003705427,0.0006102717],"genre_scores_gemma":[0.9893718,0.00000944338,0.0101455,0.00001220679,0.0002391633,0.000009554723,0.000007879768,0.00003108209,0.0001733996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02442387,"threshold_uncertainty_score":0.309347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494493374058449,"score_gpt":0.2626997091657417,"score_spread":0.2477547754251572,"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."}}