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Hum Mutat. 2019 Sep;40(9):1495-1506. doi: 10.1002/humu.23838. Epub 2019 Jul 3.

Assessment of methods for predicting the effects of PTEN and TPMT protein variants.

Author information

1
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington.
2
The eScience Institute, University of Washington, Seattle, Washington.
3
Department of Pharmacy and Biotechnology, Biocomputing Group, University of Bologna, Bologna, Italy.
4
School of Information and Communication Technology, Griffith University, Southport, Australia.
5
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
6
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
7
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
8
Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas.
9
Department of Pharmacology, Baylor College of Medicine, Houston, Texas.
10
Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas.
11
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
12
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.
13
Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Australia.
14
Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
15
Department of Genetics, Human Genetics Institute, Rutgers University, Piscataway, New Jersey.
16
Institute for Advanced Study at Technische Universität München (TUM-IAS), Garching/Munich, Germany.

Abstract

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation, we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 nonsynonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerging as top performers depending on the metric, it is nontrivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear as to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

KEYWORDS:

CAGI; VAMP-seq; phosphatase and tensin homolog, PTEN; thiopurine S-methyl transferase, TPMT; variant stability profiling

PMID:
31184403
PMCID:
PMC6744362
[Available on 2020-09-01]
DOI:
10.1002/humu.23838

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