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Assessment of predicted enzymatic activity of α‐N‐acetylglucosaminidase variants of unknown significance for CAGI 2016
Authors:Wyatt T. Clark  Laura Kasak  Constantina Bakolitsa  Zhiqiang Hu  Gaia Andreoletti  Giulia Babbi  Yana Bromberg  Rita Casadio  Roland Dunbrack  Lukas Folkman  Colby T. Ford  David Jones  Panagiotis Katsonis  Kunal Kundu  Olivier Lichtarge  Pier L. Martelli  Sean D. Mooney  Conor Nodzak  Lipika R. Pal  Predrag Radivojac  Castrense Savojardo  Xinghua Shi  Yaoqi Zhou  Aneeta Uppal  Qifang Xu  Yizhou Yin  Vikas Pejaver  Meng Wang  Liping Wei  John Moult  Guoying Karen Yu  Steven E. Brenner  Jonathan H. LeBowitz
Abstract:The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α‐N‐acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population‐scale analysis of disease epidemiology and rare variant association analysis.
Keywords:CAGI  critical assessment  enzymatic activity  machine learning  Sanfilippo syndrome  variants of unknown significance  α  ‐N‐acetylglucosaminidase, NAGLU
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