{"id":"W4390023653","doi":"10.1101/2023.12.19.572475","title":"Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Virginia and D.K. Ludwig Fund for Cancer Research","keywords":"Computer science; Computational biology; Artificial intelligence; Protein folding; Inverse; Sequence (biology); Protein design; Folding (DSP implementation); Protein engineering; Protein sequencing; Language model; Protein structure; Function (biology); Peptide sequence; Machine learning; Biology; Mathematics; Genetics; Biochemistry; Engineering; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006556872,0.0004753271,0.0005221427,0.0001850374,0.000202477,0.0004616611,0.0005596285,0.000747635,0.0006026973],"category_scores_gemma":[0.001211428,0.000334422,0.0008133616,0.0001568761,0.0005408414,0.0005446557,0.0005678312,0.001020026,0.0003092501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005417985,"about_ca_system_score_gemma":0.0005465267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001334279,"about_ca_topic_score_gemma":0.001905909,"domain_scores_codex":[0.9997835,0.00009840944,0.000009626267,0.00005238157,0.00003891517,0.00001714285],"domain_scores_gemma":[0.9996574,0.0001780624,0.00004801735,0.00006595193,0.00003448166,0.00001608297],"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":[0.00008636366,0.00009228747,0.001251908,0.0000679318,0.00006475134,0.0001011261,0.00008819369,0.9040613,0.0570988,0.01132485,0.0005291299,0.02523325],"study_design_scores_gemma":[0.000004463031,0.0000189693,0.00004806895,0.000001151737,0.000003244408,0.00001448628,0.000002606085,0.9945037,0.003639563,0.001522654,0.0002379509,0.000003060153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1854642,0.0001297601,0.8108425,0.0002700512,0.00002352424,0.00003799946,0.00007113019,0.0009068087,0.002253899],"genre_scores_gemma":[0.7582086,0.0001191131,0.2382112,0.0002345907,0.00001949803,0.0001034268,0.0002575421,0.0002228157,0.002623208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001334279,"threshold_uncertainty_score":0.003930986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502527081235531,"score_gpt":0.2380155161854131,"score_spread":0.2229902453730578,"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."}}