{"id":"W2752722581","doi":"10.1063/1.4986933","title":"Multisequence algorithm for coarse-grained biomolecular simulations: Exploring the sequence-structure relationship of proteins","year":2017,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maxima and minima; Sequence (biology); Algorithm; Folding (DSP implementation); Statistical physics; Protein folding; Space (punctuation); Sampling (signal processing); Energy landscape; Computer science; Biological system; Physics; Mathematics; Chemistry; Biology; Thermodynamics","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":[0.001109475,0.0005890353,0.0009504493,0.0005390836,0.000633053,0.0005620145,0.001883193,0.001114417,0.001869628],"category_scores_gemma":[0.002809565,0.0003568401,0.0007041083,0.0006366901,0.0007034332,0.001010675,0.001135084,0.001065569,0.0003953424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006925332,"about_ca_system_score_gemma":0.001393754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003904701,"about_ca_topic_score_gemma":0.006426136,"domain_scores_codex":[0.9997104,0.0001237329,0.00001243715,0.00003863588,0.0000887116,0.00002618906],"domain_scores_gemma":[0.9992669,0.0003110767,0.00005666268,0.0001637798,0.0001144359,0.0000870154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009723574,0.00006041531,0.0009562518,0.00006749172,0.00006189527,0.00007997051,0.00006695016,0.9500993,0.005855479,0.02106992,0.0005412007,0.02104387],"study_design_scores_gemma":[0.00001197953,0.000009865281,0.00005361121,0.000001870341,0.000002299251,0.000006027708,0.000002384224,0.9960847,0.0005408673,0.002886102,0.0003968627,0.000003423196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05538327,0.0002111102,0.9415674,0.0001394505,0.00005397439,0.00009294304,0.0001206063,0.001034226,0.001397033],"genre_scores_gemma":[0.2540984,0.0001453763,0.7435638,0.0001132135,0.00002595667,0.000459937,0.000315132,0.000357898,0.0009203154],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003904701,"threshold_uncertainty_score":0.007763922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0469695615035267,"score_gpt":0.3040086121597131,"score_spread":0.2570390506561864,"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."}}