{"id":"W2946054052","doi":"10.1021/acscentsci.9b00221","title":"Dynamic and Functional Profiling of Xylan-Degrading Enzymes in <i>Aspergillus</i> Secretomes Using Activity-Based Probes","year":2019,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Diamond Light Source; Generalitat de Catalunya; Ministerio de Ciencia e Innovación; Agence Nationale de la Recherche; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Ministerio de Ciencia, Innovación y Universidades; Directorate for Biological Sciences; Agència de Gestió d'Ajuts Universitaris i de Recerca; Royal Society; Ministerio de Economía y Competitividad; Yorkshire Forward","keywords":"Enzyme; Aspergillus niger; Xylan; Glycoside hydrolase; Chemistry; Profiling (computer programming); Biochemistry; Computational biology; Biology; Computer science","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":[0.0001164317,0.0003139491,0.000119144,0.0001674899,0.0001055087,0.0004006239,0.0001276442,0.0002320503,0.0007023154],"category_scores_gemma":[0.0001455022,0.00009819636,0.0001888974,0.0001604711,0.0001708612,0.0002898987,0.0001516369,0.0003421471,0.000321469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001971792,"about_ca_system_score_gemma":0.0001209042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004294774,"about_ca_topic_score_gemma":0.0006397109,"domain_scores_codex":[0.9999139,0.00001070755,0.000006571445,0.00003205721,0.00002124735,0.00001551712],"domain_scores_gemma":[0.9999166,0.00001952609,0.00002699003,0.00001064636,0.00001241517,0.00001390742],"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":[0.00001649682,0.000002782766,0.0000674667,0.000009734711,0.000001292752,0.000007431474,0.000004855925,0.000026113,0.9993056,0.00003844238,0.00001315327,0.0005065791],"study_design_scores_gemma":[0.00000137291,0.00002901196,0.001874174,0.00000219878,0.000005540728,0.0000558029,0.0000148353,0.0007814483,0.9963288,0.00006270924,0.0008404415,0.000003580476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655946,0.001032993,0.02738231,0.0002143751,0.00003513609,0.00004814626,0.001952569,0.0002058099,0.003534066],"genre_scores_gemma":[0.9827625,0.0008416813,0.01249116,0.0001300735,0.00001406804,0.00005807489,0.001297673,0.00005705894,0.00234777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007023154,"threshold_uncertainty_score":0.002349496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009957828874791446,"score_gpt":0.2104959873561411,"score_spread":0.2005381584813497,"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."}}