{"id":"W1979955528","doi":"10.1074/jbc.m701996200","title":"TrkA Receptor “Hot Spots” for Binding of NT-3 as a Heterologous Ligand","year":2007,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Nerve injury and regeneration","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Cancer Institute","keywords":"Tropomyosin receptor kinase A; Trk receptor; Tropomyosin receptor kinase B; Low-affinity nerve growth factor receptor; Neurotrophin; Tropomyosin receptor kinase C; Allosteric regulation; Chemistry; Binding site; Receptor; Docking (animal); Nerve growth factor; Receptor tyrosine kinase; Cell biology; Biology; Biochemistry; Neurotrophic factors; Growth factor; Platelet-derived growth factor receptor","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000128465,0.0002538679,0.0002666637,0.000147079,0.0002260623,0.0002851746,0.0003626292,0.0002449317,0.003089542],"category_scores_gemma":[0.0001204158,0.0001543089,0.0004354821,0.0001016392,0.0001927499,0.0001697346,0.0003043599,0.0004490168,0.001123299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006712257,"about_ca_system_score_gemma":0.0002536938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008836224,"about_ca_topic_score_gemma":0.001357853,"domain_scores_codex":[0.9997734,0.00003787719,0.00001080715,0.00004902149,0.00006842385,0.00006043851],"domain_scores_gemma":[0.9999067,0.00002081889,0.00001631587,0.00002028123,0.00001028079,0.00002563199],"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":[0.00009761233,0.00001719767,0.0001263836,0.00002445114,0.000004585641,0.00003681436,0.00001033128,0.0001140234,0.9985524,0.0002247209,0.00005083503,0.0007405297],"study_design_scores_gemma":[0.00002662207,0.0001424503,0.004625075,0.000008243388,0.00001093078,0.0005639066,0.00003110109,0.002106735,0.9888055,0.00009166355,0.003579295,0.000008464022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896409,0.001373463,0.005862382,0.00005378854,0.00003330725,0.00002865094,0.0001484543,0.00008858266,0.002770528],"genre_scores_gemma":[0.9933776,0.0002895538,0.003419436,0.00005314028,0.000005524079,0.0000213179,0.0003115077,0.00001185868,0.002509981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003089542,"threshold_uncertainty_score":0.01033556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05458571786920097,"score_gpt":0.3100585467979436,"score_spread":0.2554728289287427,"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."}}