{"id":"W4285592040","doi":"10.1021/acschemneuro.1c00567","title":"Optimizing Epitope Conformational Ensembles Using α-Synuclein Cyclic Peptide “Glycindel” Scaffolds: A Customized Immunogen Method for Generating Oligomer-Selective Antibodies for Parkinson’s Disease","year":2022,"lang":"en","type":"article","venue":"ACS Chemical Neuroscience","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Canadian Institutes of Health Research; Alberta Innovates; Compute Canada","keywords":"Immunogen; Epitope; Oligomer; Antibody; Alpha-synuclein; Parkinson's disease; Peptide; Cyclic peptide; Chemistry; Disease; Synucleinopathies; Computational biology; Monoclonal antibody; Biophysics; Neuroscience; Biology; Medicine; Biochemistry; Immunology; Pathology","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":[0.0002224113,0.0003241239,0.0002476679,0.0002004056,0.000146727,0.0002230063,0.0002303889,0.0002076285,0.0003492384],"category_scores_gemma":[0.0003271611,0.0001212276,0.0002078139,0.000157442,0.000143228,0.0002218625,0.0002422234,0.0002321749,0.0001047773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650016,"about_ca_system_score_gemma":0.0002102785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000379477,"about_ca_topic_score_gemma":0.0008112469,"domain_scores_codex":[0.9998982,0.00001499529,0.000005053913,0.0000256945,0.00004000631,0.0000160457],"domain_scores_gemma":[0.9998989,0.00002752366,0.00003069622,0.000009469672,0.00001314403,0.00002023613],"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":[0.0001748854,0.0001700929,0.001733927,0.0000686175,0.00003298621,0.0001254844,0.00004223151,0.07765923,0.9059526,0.0006553563,0.00010905,0.01327563],"study_design_scores_gemma":[0.00004277169,0.001126708,0.002135762,0.000007127295,0.00003620748,0.0001746308,0.00003600449,0.3597966,0.635306,0.000317142,0.0009956734,0.00002537195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816165,0.0001325466,0.0175595,0.0000204243,0.000005791898,0.00002499954,0.0000477941,0.0000832123,0.0005092162],"genre_scores_gemma":[0.980724,0.00009402206,0.01876591,0.00001544795,0.000002420652,0.00003737697,0.00009091984,0.00001796191,0.0002518237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000379477,"threshold_uncertainty_score":0.001922727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618598867269753,"score_gpt":0.3123339694128637,"score_spread":0.2761479807401662,"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."}}