Assessment of methods for predicting the effects of PTEN and TPMT protein variants.
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
Year published
2019
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
Hum Mutat
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