{"id":"W4403830519","doi":"10.1039/d4cp03987d","title":"Decoding the enigma of RNA–protein recognition: quantum chemical insights into arginine fork motifs","year":2024,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Shared Hierarchical Academic Research Computing Network; University Grants Commission","keywords":"Decoding methods; Arginine; Quantum; Computational biology; RNA; Chemistry; Biophysics; Biology; Nanotechnology; Physics; Biochemistry; Computer science; Materials science; Quantum mechanics; Amino acid; Gene; Algorithm","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":[0.0004785613,0.0001867215,0.0002061056,0.0003267995,0.0004334096,0.0004738193,0.000670803,0.0007856183,0.002871107],"category_scores_gemma":[0.001056638,0.0001533761,0.0002094665,0.0001777392,0.0009802846,0.002096004,0.0005024282,0.0009429362,0.0003732448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004493925,"about_ca_system_score_gemma":0.0002884315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004698784,"about_ca_topic_score_gemma":0.0006562168,"domain_scores_codex":[0.9999156,0.00002124776,0.000003818425,0.00001767806,0.00002830673,0.00001338321],"domain_scores_gemma":[0.999754,0.0001411079,0.00002913242,0.00003789603,0.00001646483,0.00002145101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001989036,0.0002391695,0.003148437,0.0005945961,0.00009525271,0.0003484156,0.000371359,0.1002653,0.1886275,0.6180817,0.00436885,0.08366045],"study_design_scores_gemma":[0.00002658547,0.0001054844,0.001297735,0.0000343885,0.00001965419,0.0001316584,0.0001843724,0.4242826,0.01854013,0.5461326,0.00921219,0.00003267332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5520386,0.01270709,0.3967867,0.01018526,0.0005694064,0.00005080066,0.0002909727,0.0005703157,0.02680078],"genre_scores_gemma":[0.9689113,0.002056804,0.02733288,0.0002626101,0.00007579561,0.00001832829,0.00007383237,0.00004326153,0.001225241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002871107,"threshold_uncertainty_score":0.009604752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474340224241221,"score_gpt":0.2452464650264606,"score_spread":0.2305030627840484,"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."}}