{"id":"W4366816341","doi":"10.1073/pnas.2303149120","title":"Exploiting conformational dynamics to modulate the function of designed proteins","year":2023,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Government of Canada; Amgen","keywords":"Protein dynamics; Nuclear magnetic resonance spectroscopy; Chemistry; Function (biology); Protein design; Molecular dynamics; Protein structure; Mutagenesis; Molecule; Biophysics; Peptide sequence; Computational biology; Biochemistry; Stereochemistry; Biology; Computational chemistry; Mutation; Genetics","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":[0.0002021329,0.0003025802,0.0001556061,0.0001091862,0.0001529658,0.0003120486,0.0002296847,0.0002148425,0.000569279],"category_scores_gemma":[0.000306624,0.0001505878,0.0001111068,0.00009479884,0.000252649,0.0002365069,0.0001782761,0.0004243673,0.0001454915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003695553,"about_ca_system_score_gemma":0.0002069656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002657459,"about_ca_topic_score_gemma":0.0004444715,"domain_scores_codex":[0.9999174,0.00001310133,0.000007693903,0.00002537389,0.00002050683,0.00001586158],"domain_scores_gemma":[0.9998903,0.00003001293,0.00003966372,0.00001205347,0.00001175177,0.00001610985],"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":[0.00003694081,0.00002169388,0.0001165049,0.0000165696,0.0000034816,0.00001845441,0.00001464945,0.001219204,0.9964125,0.000380254,0.00002642628,0.001733374],"study_design_scores_gemma":[0.00001943351,0.0001772238,0.0005194765,0.000003317798,0.000008300972,0.00006485908,0.00001091507,0.01623228,0.9812971,0.0001536533,0.001503729,0.000009566469],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767929,0.0001886095,0.0216247,0.0001028163,0.00002324396,0.00004102175,0.00005951736,0.0001451665,0.001021948],"genre_scores_gemma":[0.9834616,0.0002415841,0.01539522,0.00007204289,0.000005844856,0.00003896279,0.00005827127,0.00004627987,0.0006801884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000569279,"threshold_uncertainty_score":0.002681255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410475273206473,"score_gpt":0.2766010003422064,"score_spread":0.2524962476101417,"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."}}