{"id":"W2053947235","doi":"10.1002/prot.1154","title":"Speeding protein folding beyond the Gō model: How a little frustration sometimes helps","year":2001,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Frustration; Energy landscape; Protein folding; Statistical physics; Folding (DSP implementation); Physics; Kinetics; Chemical physics; Hamiltonian (control theory); Thermodynamics; Chemistry; Classical mechanics; Condensed matter physics; Mathematics; Engineering; Nuclear magnetic resonance; Mathematical optimization","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":[0.0001824087,0.0002789958,0.0001699284,0.00008239224,0.0004095834,0.0002483758,0.0001708126,0.0002772831,0.0000220836],"category_scores_gemma":[0.00008885127,0.0001885709,0.00008068608,0.0002024739,0.0001135012,0.00005827147,0.0001213322,0.0002412985,0.00000223134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002101031,"about_ca_system_score_gemma":0.00007066577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006216565,"about_ca_topic_score_gemma":0.00003866585,"domain_scores_codex":[0.9988604,0.00002944388,0.0002814337,0.0002474794,0.0002613878,0.0003199071],"domain_scores_gemma":[0.9992374,0.000006969789,0.0002074199,0.0003327224,0.0001164489,0.00009908097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005314942,0.00004266264,0.001284111,0.0003322976,0.0002340248,0.00000262031,0.000847101,0.001274692,0.881754,0.01142378,0.002474533,0.09979865],"study_design_scores_gemma":[0.005146189,0.003361144,0.002336551,0.0002210784,0.0004122596,0.0007855469,0.004287817,0.4201236,0.3659103,0.1171088,0.0773835,0.002923217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7152418,0.0006708374,0.2777731,0.002158368,0.0003002243,0.001852492,0.00006976593,0.00008751915,0.001845845],"genre_scores_gemma":[0.9707257,0.0001030649,0.02687618,0.0005650922,0.000429787,0.00005437967,0.0001775176,0.0000253693,0.0010429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5158437,"threshold_uncertainty_score":0.7689697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007994913296369865,"score_gpt":0.2081611389460154,"score_spread":0.2001662256496455,"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."}}