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

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    Author(s)
    Pejaver, Vikas
    Babbi, Giulia
    Casadio, Rita
    Folkman, Lukas
    Katsonis, Panagiotis
    Kundu, Kunal
    Lichtarge, Olivier
    Martelli, Pier Luigi
    Miller, Maximilian
    Moult, John
    Pal, Lipika R
    Savojardo, Castrense
    Yin, Yizhou
    Zhou, Yaoqi
    Radivojac, Predrag
    Bromberg, Yana
    Griffith University Author(s)
    Zhou, Yaoqi
    Folkman, Lukas
    Year published
    2019
    Metadata
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    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 (CAGI), 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 non-synonymous variants from two ...
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    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 (CAGI), 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 non-synonymous 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 emerged as top-performers depending on the metric, it is non-trivial 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 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. This article is protected by copyright. All rights reserved.
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    Journal Title
    Hum Mutat
    DOI
    https://doi.org/10.1002/humu.23838
    Copyright Statement
    © 2019 Wiley Periodicals Inc. This is the peer reviewed version of the following article: Assessment of methods for predicting the effects of PTEN and TPMT protein variants, Human Mutation, pp., 1-12, 2019, which has been published in final form at 10.1002/humu.23838. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
    Subject
    Genetics
    Clinical Sciences
    Publication URI
    http://hdl.handle.net/10072/386111
    Collection
    • Journal articles

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