{"id":"W2157362913","doi":"10.1002/pro.2071","title":"The interface of protein structure, protein biophysics, and molecular evolution","year":2012,"lang":"en","type":"review","venue":"Protein Science","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Université de Montréal","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Center for Advancing Translational Sciences; Engineering and Physical Sciences Research Council","keywords":"Protein structure; Computational biology; Protein structure prediction; Protein evolution; Protein domain; Biology; Structural genomics; Protein engineering; Protein design; Structural biology; Protein folding; Protein sequencing; Molecular evolution; Inference; Function (biology); Population; Peptide sequence; Genetics; Computer science; Biochemistry; Phylogenetics; Gene; Artificial intelligence","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":[0.0005634568,0.0003293032,0.0004601706,0.00006296982,0.0002909056,0.0000632385,0.0007423381,0.0001611002,0.000001286888],"category_scores_gemma":[0.0001424718,0.0002159489,0.0001297526,0.0003232846,0.001249899,0.000003175228,0.0007239673,0.0001659907,0.000002577674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003757809,"about_ca_system_score_gemma":0.0003797363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002103165,"about_ca_topic_score_gemma":0.00000541086,"domain_scores_codex":[0.9982201,0.0001081271,0.000388395,0.0005638072,0.0002907674,0.0004287501],"domain_scores_gemma":[0.9986503,0.000008101553,0.0004136818,0.0006534829,0.0001804174,0.00009397545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006016974,0.00001360671,0.000002244193,0.001082571,0.00004150899,1.897458e-7,0.00001437205,5.146849e-7,0.7802629,0.001564861,0.000002216222,0.217009],"study_design_scores_gemma":[0.0001210462,0.0002650589,0.00001563671,0.001369583,0.0001143045,0.00001135689,0.00002391412,0.000004208004,0.4251556,0.001718417,0.5707162,0.000484668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03213729,0.9647478,0.0003687453,0.00001769047,0.00008325072,0.002485171,0.00004850881,0.000002983249,0.0001085576],"genre_scores_gemma":[0.4766135,0.5204373,0.001971263,0.000004989474,0.0001597694,0.0005058006,0.000007975952,0.00004462304,0.0002548063],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.570714,"threshold_uncertainty_score":0.880614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680977511915112,"score_gpt":0.3000663059869604,"score_spread":0.2832565308678093,"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."}}