{"id":"W3134857564","doi":"10.1016/j.xpro.2021.100362","title":"Functional proteomics protocol for the identification of interaction partners in Tetrahymena thermophila","year":2021,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Protist diversity and phylogeny","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec; Toronto Metropolitan University; York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Tetrahymena; Ciliate; Computational biology; Tandem affinity purification; Biology; Proteomics; Tandem mass spectrometry; Cleavage (geology); Epitope; Identification (biology); Chemistry; Cell biology; Mass spectrometry; Biochemistry; Affinity chromatography; Genetics; Antibody; Chromatography; Gene; Enzyme","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":[0.001027751,0.00176117,0.00158883,0.00142558,0.001836244,0.0007596651,0.001649867,0.0009926836,0.01615021],"category_scores_gemma":[0.0007912668,0.001377993,0.001002988,0.0015598,0.0005516962,0.0007281212,0.001119247,0.00272843,0.02075216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007384908,"about_ca_system_score_gemma":0.0009482675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001518692,"about_ca_topic_score_gemma":0.003082294,"domain_scores_codex":[0.998914,0.0001583558,0.000141793,0.0002584515,0.0003338075,0.0001936321],"domain_scores_gemma":[0.9994935,0.0001051018,0.00003484529,0.0001742762,0.0001281683,0.00006414705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001918503,0.0001094248,0.0001690674,0.0005384036,0.000039258,0.0002450435,0.0001584698,0.0003469476,0.9821315,0.001061448,0.006626378,0.008382249],"study_design_scores_gemma":[0.0002337883,0.000586651,0.008119545,0.0002697068,0.0001481269,0.001294253,0.0001432265,0.004751438,0.6338691,0.00228308,0.3481197,0.0001814144],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.1274989,0.008587377,0.6991928,0.001557941,0.001754309,0.01776246,0.1031506,0.01153245,0.02896324],"genre_scores_gemma":[0.1100875,0.00851154,0.5383609,0.001377137,0.0002661819,0.05181883,0.2384923,0.003131477,0.04795413],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.01615021,"threshold_uncertainty_score":0.0540278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05684407781350478,"score_gpt":0.3610884090484498,"score_spread":0.304244331234945,"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."}}