{"id":"W3027038845","doi":"10.3389/fmolb.2020.00098","title":"Bio-Layer Interferometry Analysis of the Target Binding Activity of CRISPR-Cas Effector Complexes","year":2020,"lang":"en","type":"article","venue":"Frontiers in Molecular Biosciences","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Max Planck Research School for Advanced Methods in Process and Systems Engineering; University of Toronto; Deutsche Forschungsgemeinschaft; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; Justus Liebig Universität Gießen","keywords":"CRISPR; Effector; Biotinylation; Microscale thermophoresis; Nucleic acid; Computational biology; Chemistry; CRISPR interference; Biology; Biochemistry; Gene; Genome editing","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":[0.0007854417,0.0006112008,0.0003967117,0.000880439,0.0003498831,0.0005019495,0.0006145226,0.0006593924,0.001023483],"category_scores_gemma":[0.0006917568,0.0002762611,0.0003499163,0.000749909,0.0002719844,0.0004446263,0.0003037045,0.0008145603,0.0005611235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005283187,"about_ca_system_score_gemma":0.0002452575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078488,"about_ca_topic_score_gemma":0.0011867,"domain_scores_codex":[0.999148,0.0002362162,0.00005748423,0.0001382894,0.0003122654,0.0001076913],"domain_scores_gemma":[0.9994547,0.000284799,0.00008533604,0.00003690636,0.000101232,0.00003703795],"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":[0.00002454554,0.00002082291,0.0003575641,0.00003074575,0.000007267532,0.00001213828,0.00002601516,0.0001212464,0.9981368,0.00009179537,0.00003855168,0.00113252],"study_design_scores_gemma":[0.000003477329,0.00005722792,0.004220693,0.000003457113,0.00001219075,0.00004553145,0.00003752276,0.006002766,0.9889176,0.0000668478,0.0006232885,0.000009568928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8724012,0.001813447,0.1182168,0.0002812795,0.00006315648,0.0001268925,0.001171266,0.0009553963,0.004970517],"genre_scores_gemma":[0.8886703,0.001562968,0.1045329,0.0001750981,0.00001930614,0.0003100125,0.00141935,0.00009727739,0.003212822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001078488,"threshold_uncertainty_score":0.004153848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009712974658078773,"score_gpt":0.2827868943051011,"score_spread":0.2730739196470223,"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."}}