{"id":"W2753413310","doi":"10.1002/pro.3286","title":"Engineered, highly reactive substrates of microbial transglutaminase enable protein labeling within various secondary structure elements","year":2017,"lang":"en","type":"article","venue":"Protein Science","topic":"Blood properties and coagulation","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; PROTEO; Centre in Green Chemistry and Catalysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Lysine; Tissue transglutaminase; Glutamine; Fluorophore; Biochemistry; Protein secondary structure; Substrate (aquarium); Active site; Protein engineering; Protein structure; Reactivity (psychology); Protein design; Enzyme; Stereochemistry; Combinatorial chemistry; Amino acid; Fluorescence; Biology","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":[0.0001630635,0.0005829812,0.0002966778,0.0001584036,0.00008549068,0.0002577959,0.0003320616,0.0003236712,0.0003864425],"category_scores_gemma":[0.0001693156,0.0001512578,0.0002116766,0.0002552531,0.0002308782,0.0001870666,0.0002378001,0.0005558801,0.0003269824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672047,"about_ca_system_score_gemma":0.0001156268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000400158,"about_ca_topic_score_gemma":0.0007353853,"domain_scores_codex":[0.9998155,0.00003538126,0.00001293972,0.00004452549,0.00005453119,0.00003695872],"domain_scores_gemma":[0.9998869,0.00001611891,0.00004658582,0.0000157112,0.00001432607,0.00002040235],"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":[0.00001629905,0.000007572495,0.00008067789,0.00001050225,0.000001950869,0.00001851777,0.000006495792,0.00007940332,0.9993528,0.00004886054,0.000007285106,0.0003696307],"study_design_scores_gemma":[0.000003916191,0.00009069386,0.0004622622,0.000001431191,0.000003959498,0.00009684575,0.000005738925,0.0003038384,0.9984627,0.0000141235,0.000552449,0.000002134189],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880057,0.0004657654,0.01041551,0.00004168997,0.00001062425,0.00003260639,0.0001688196,0.00009881723,0.0007605345],"genre_scores_gemma":[0.9850713,0.0004192419,0.01284557,0.00003631004,0.00000283531,0.00002826413,0.0004509666,0.00004041637,0.001105086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005829812,"threshold_uncertainty_score":0.00193876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148107714537577,"score_gpt":0.2481774866604253,"score_spread":0.2333667152066676,"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."}}