{"id":"W4402350565","doi":"10.1021/acschembio.4c00485","title":"Protein Visualizer 2.0: Intuitive and Interactive Visualization of Protein Topology and Co/Post-Translational Modifications","year":2024,"lang":"en","type":"letter","venue":"ACS Chemical Biology","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Visualization; Computer science; Topology (electrical circuits); Computational biology; UniProt; Glycosylation; Protein–protein interaction; Protein structure; Bioinformatics; Data mining; Chemistry; Biology; Biochemistry; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001626227,0.0002957791,0.0003588925,0.0001944614,0.00005385327,0.00002840994,0.0001770619,0.001282102,0.00007977796],"category_scores_gemma":[0.0003538787,0.0002698148,0.00007771957,0.0001267055,0.0007635133,0.00000842893,0.0001938188,0.0006636521,0.00001490463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053364,"about_ca_system_score_gemma":0.0001443636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005208963,"about_ca_topic_score_gemma":0.000006994615,"domain_scores_codex":[0.9981533,0.0002321276,0.0004268578,0.0007375974,0.0001483048,0.0003017628],"domain_scores_gemma":[0.9990872,0.00007950456,0.0001694147,0.0002528098,0.0003427941,0.00006834108],"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":[0.0001619636,0.00003568615,0.0000534891,0.0002292192,0.0001585559,0.000005376233,0.000127087,1.29588e-7,0.9845282,0.002903262,0.009898983,0.001897979],"study_design_scores_gemma":[0.0004567181,0.0004314994,0.00003490336,0.00008188296,0.00003260661,0.0000316603,0.00003990587,0.00005088715,0.7120945,0.002384284,0.2840576,0.0003035234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8542154,0.002361869,0.002979822,0.1350818,0.0001163597,0.002684218,0.00117809,0.00005148959,0.001331003],"genre_scores_gemma":[0.9718296,0.0001350432,0.0004043531,0.01608204,0.0007002978,0.0005194826,0.008471918,0.00006447672,0.001792777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2741587,"threshold_uncertainty_score":0.9999754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657903899295407,"score_gpt":0.3367826147348681,"score_spread":0.320203575741914,"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."}}