{"id":"W4408586797","doi":"10.1093/protein/gzaf003","title":"Tuning ProteinMPNN to reduce protein visibility via MHC Class I through direct preference optimization","year":2025,"lang":"en","type":"article","venue":"Protein Engineering Design and Selection","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Asian Studies Association; University of Victoria","funders":"Infrastruktura PL-Grid; Fundacja na rzecz Nauki Polskiej; European Commission; Royal Academy of Engineering; European Regional Development Fund; Horizon 2020 Framework Programme; UK Research and Innovation; Intelligence Community Postdoctoral Research Fellowship Program; Israel Cancer Research Fund","keywords":"MHC class I; Computer science; Visibility; Major histocompatibility complex; Epitope; Workflow; Computational biology; Immune system; Antigen; Biology; Genetics; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005660249,0.0007281476,0.0003617688,0.0002678467,0.0002241745,0.0004842094,0.0006794633,0.0004674158,0.001439163],"category_scores_gemma":[0.001028597,0.000255924,0.0004183153,0.0002306237,0.0002648919,0.0004772546,0.0005082466,0.0005557574,0.0004357192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006028883,"about_ca_system_score_gemma":0.0006165939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008621037,"about_ca_topic_score_gemma":0.001741538,"domain_scores_codex":[0.9997459,0.0000570781,0.0000112444,0.00005880712,0.00008447986,0.00004254056],"domain_scores_gemma":[0.9997755,0.00008944185,0.00004017915,0.00002878199,0.00004701958,0.00001907839],"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":[0.0002627332,0.0002309804,0.00324634,0.0002571456,0.00007130888,0.00016625,0.00006285441,0.6525998,0.2293584,0.005994306,0.001852338,0.1058975],"study_design_scores_gemma":[0.00001574691,0.0001010437,0.0002980064,0.000005619303,0.00001446323,0.00004846561,0.00001561041,0.9644545,0.03120491,0.001418805,0.00241319,0.000009681861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4002796,0.0006079805,0.5865817,0.000193139,0.00008526731,0.0001063303,0.0001553177,0.002335052,0.009655604],"genre_scores_gemma":[0.8258708,0.0002168895,0.1706566,0.0001926998,0.00001234194,0.0002007433,0.0002216561,0.0004385298,0.002189894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001439163,"threshold_uncertainty_score":0.004814446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543434391653663,"score_gpt":0.2233008051834782,"score_spread":0.2078664612669415,"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."}}