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Hum Mutat. 2019 Sep;40(9):1519-1529. doi: 10.1002/humu.23875.

Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016.

Author information

1
Human Genetics, BioMarin Pharmaceutical, San Rafael, California.
2
Department of Plant and Microbial Biology, University of California, Berkeley, California.
3
Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.
4
Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
5
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
6
Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania.
7
Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland.
8
Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, North Carolina.
9
Bioinformatics Group, Department of Computer Science, University College London, London, UK.
10
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
11
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
12
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
13
Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas.
14
Department of Pharmacology, Baylor College of Medicine, Houston, Texas.
15
Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas.
16
Buck Institute for Research on Aging, Novato, California.
17
Department of Computer Science, Indiana University, Bloomington, Indiana.
18
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana.
19
Department of Informatics, Indiana University, Bloomington, Indiana.
20
Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, P.R. China.
21
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.

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; Sanfilippo syndrome; critical assessment; enzymatic activity; machine learning; variants of unknown significance; α-N-acetylglucosaminidase, NAGLU

PMID:
31342580
DOI:
10.1002/humu.23875

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