{"id":"W4412990593","doi":"10.1038/s41467-025-60526-6","title":"Characterizing and engineering post-translational modifications with high-throughput cell-free expression","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Peptidase Inhibition and Analysis","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; National Institute of Allergy and Infectious Diseases; Division of Electrical, Communications and Cyber Systems; U.S. Department of Defense; International Institute for Nanotechnology, Northwestern University; National Science Foundation; Canadian Institutes of Health Research; Northwestern University; Defense Threat Reduction Agency; Defense Advanced Research Projects Agency; Foundation for the National Institutes of Health; National Institutes of Health; Government of Canada","keywords":"Throughput; Posttranslational modification; Computational biology; Computer science; Cell biology; Biology; Biochemistry; Telecommunications; Enzyme","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":[0.001569521,0.0008770633,0.0007936074,0.000478416,0.000388886,0.001359406,0.0006191384,0.0006304014,0.001251866],"category_scores_gemma":[0.00133313,0.0004737006,0.0007917001,0.0006566954,0.000546209,0.000697119,0.0007783822,0.001895186,0.001578121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007751856,"about_ca_system_score_gemma":0.0009428846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008140202,"about_ca_topic_score_gemma":0.001553154,"domain_scores_codex":[0.9988849,0.0001527014,0.0001114086,0.0002098415,0.0005328185,0.0001082558],"domain_scores_gemma":[0.9991581,0.0002222179,0.0001874079,0.000227031,0.0001368337,0.00006839008],"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":[0.00004571028,0.00005332765,0.0002736237,0.00008517916,0.00001564716,0.00004157049,0.00003387935,0.001021943,0.9911413,0.0004354422,0.0003063531,0.006546025],"study_design_scores_gemma":[0.000006808358,0.0000867139,0.0003842402,0.000005748071,0.0000126365,0.00006436494,0.00001139961,0.002473178,0.991367,0.00016276,0.005414564,0.0000105135],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3847422,0.001669974,0.5963547,0.0007865704,0.0002523939,0.001126236,0.004540681,0.004969496,0.005557746],"genre_scores_gemma":[0.5075042,0.003718591,0.4715549,0.000346837,0.00005522882,0.0008777482,0.007350603,0.001260884,0.007331001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001569521,"threshold_uncertainty_score":0.008300543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014942059407395,"score_gpt":0.2657892202550515,"score_spread":0.2556397996609776,"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."}}