{"id":"W2021491872","doi":"10.1021/ja310585e","title":"Using Distal-Site Mutations and Allosteric Inhibition To Tune, Extend, and Narrow the Useful Dynamic Range of Aptamer-Based Sensors","year":2012,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Aptamer; Allosteric regulation; Chemistry; Dynamic range; Oligonucleotide; Folding (DSP implementation); Range (aeronautics); Computational biology; Biophysics; Nanotechnology; Biological system; Computer science; Receptor; Biochemistry; Biology; DNA; Genetics; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001899852,0.00009549232,0.0001720532,0.00001342573,0.00007423232,0.00001102846,0.00006812435,0.00004036328,3.225207e-7],"category_scores_gemma":[0.00006328625,0.00005523588,0.0001902286,0.0001672977,0.0004712805,0.000007125456,0.00007428503,0.0001093329,4.71282e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002638791,"about_ca_system_score_gemma":0.00001733215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001062914,"about_ca_topic_score_gemma":0.000001592065,"domain_scores_codex":[0.9993849,0.00005602974,0.0002053766,0.00009552067,0.0001277547,0.0001304333],"domain_scores_gemma":[0.9993151,0.00003022569,0.0003651348,0.0001348809,0.00008588997,0.00006879792],"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":[0.0000442467,0.00003232625,0.003656064,0.000007313966,0.00004277795,2.260761e-7,0.000111229,0.00002200875,0.9952379,4.523263e-7,0.0001011237,0.0007442571],"study_design_scores_gemma":[0.0002190116,0.0001265766,0.006608529,0.00003444943,0.0001504743,0.0001004683,0.0005423875,0.0005356169,0.9912177,0.00002068071,0.0003276933,0.000116445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973319,0.0002436298,0.001524899,0.0007991001,0.0000165365,0.00006147741,0.00001567587,0.000003000954,0.000003733763],"genre_scores_gemma":[0.9876034,0.00006091039,0.01172878,0.0005187221,0.00006631343,7.279006e-7,0.000003391805,0.00000921111,0.000008509863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01020388,"threshold_uncertainty_score":0.2252454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158484119037985,"score_gpt":0.2843530208526248,"score_spread":0.272768179662245,"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."}}