| Literature DB >> 31342580 |
Wyatt T Clark1, Laura Kasak2,3, Constantina Bakolitsa2, Zhiqiang Hu2, Gaia Andreoletti2, Giulia Babbi4, Yana Bromberg5, Rita Casadio4, Roland Dunbrack6, Lukas Folkman7, Colby T Ford8, David Jones9, Panagiotis Katsonis10, Kunal Kundu11,12, Olivier Lichtarge10,13,14,15, Pier L Martelli3, Sean D Mooney16, Conor Nodzak8, Lipika R Pal11, Predrag Radivojac17, Castrense Savojardo4, Xinghua Shi8, Yaoqi Zhou18, Aneeta Uppal8, Qifang Xu6, Yizhou Yin11,12, Vikas Pejaver17,19, Meng Wang20, Liping Wei20, John Moult11,21, Guoying Karen Yu1, Steven E Brenner2, Jonathan H LeBowitz1.
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.Entities:
Keywords: CAGI; Sanfilippo syndrome; critical assessment; enzymatic activity; machine learning; variants of unknown significance; α-N-acetylglucosaminidase, NAGLU
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Year: 2019 PMID： 31342580 PMCID： PMC7156275 DOI： 10.1002/humu.23875
Source DB: PubMed Journal: Hum Mutat ISSN： 1059-7794 Impact factor: 4.878