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

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

doi:10.1002/humu.23875

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

Journal article

Open access

Peer reviewed

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

Wyatt T Clark, Laura Kasak, Constantina Bakolitsa, Zhiqiang Hu, Gaia Andreoletti, Giulia Babbi, Yana Bromberg, Rita Casadio, Roland Dunbrack, Lukas Folkman, …

Human mutation, Vol.40(9), pp.1519-1529

09/2019

DOI: https://doi.org/10.1002/humu.23875

PMCID: PMC7156275

PMID: 31342580

Abstract

Acetylglucosaminidase - genetics

Acetylglucosaminidase - metabolism

Computational Biology - methods

Humans

Models, Genetic

Mutation, Missense

Regression Analysis

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.

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Title: Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016
Creators: Wyatt T Clark - Human Genetics, BioMarin Pharmaceutical, San Rafael, California
Laura Kasak - Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
Constantina Bakolitsa - Department of Plant and Microbial Biology, University of California, Berkeley, California
Zhiqiang Hu - Department of Plant and Microbial Biology, University of California, Berkeley, California
Gaia Andreoletti - Department of Plant and Microbial Biology, University of California, Berkeley, California
Giulia Babbi - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Yana Bromberg - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Rita Casadio - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Roland Dunbrack - Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania
Lukas Folkman - Machine Learning and Computational Biology Lab, Department of Biosystems Science and Engineering, ETH, Zurich, Switzerland
Colby T Ford - Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, North Carolina
David Jones - Bioinformatics Group, Department of Computer Science, University College London, London, UK
Panagiotis Katsonis - Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
Kunal Kundu - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
Olivier Lichtarge - Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas
Pier L Martelli - Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
Sean D Mooney - Buck Institute for Research on Aging, Novato, California
Conor Nodzak - Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, North Carolina
Lipika R Pal - Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
Predrag Radivojac - Department of Computer Science, Indiana University, Bloomington, Indiana
Castrense Savojardo - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Xinghua Shi - Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, North Carolina
Yaoqi Zhou - Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
Aneeta Uppal - Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, North Carolina
Qifang Xu - Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania
Yizhou Yin - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
Vikas Pejaver - Department of Informatics, Indiana University, Bloomington, Indiana
Meng Wang - Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, P.R. China
Liping Wei - Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, P.R. China
John Moult - Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
Guoying Karen Yu - Human Genetics, BioMarin Pharmaceutical, San Rafael, California
Steven E Brenner - Department of Plant and Microbial Biology, University of California, Berkeley, California
Jonathan H LeBowitz - Human Genetics, BioMarin Pharmaceutical, San Rafael, California
Publication Details: Human mutation, Vol.40(9), pp.1519-1529
Date published: 09/2019
Grant note: R01 LM009722 / NLM NIH HHS 1121629 / National Health and Medical Research Council of Australia R01 AG061105 / NIA NIH HHS IUT34-12 / Eesti Teadusagentuur U01 GM115486 / NIGMS NIH HHS 1083450 / National Health and Medical Research Council of Australia R01 GM066099 / NIGMS NIH HHS R01 MH105524 / NIMH NIH HHS K99 LM012992 / NLM NIH HHS R01 GM115486 / NIGMS NIH HHS U41 HG007346 / NHGRI NIH HHS 1059775 / National Health and Medical Research Council of Australia R01 GM079656 / NIGMS NIH HHS R13 HG006650 / NHGRI NIH HHS
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031665452804646