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Pon-tstab : Protein variant stability predictor. importance of training data quality

Yang, Yang LU ; Urolagin, Siddhaling LU ; Niroula, Abhishek LU ; Ding, Xuesong; Shen, Bairong and Vihinen, Mauno LU (2018) In International Journal of Molecular Sciences 19(4).
Abstract

Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab,... (More)

Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Benchmark quality, Machine learning method, Mutation, Protein stability prediction, Variation interpretation
in
International Journal of Molecular Sciences
volume
19
issue
4
publisher
MOLECULAR DIVERSITY PRESERVATION INT
external identifiers
  • scopus:85044635395
ISSN
1661-6596
DOI
10.3390/ijms19041009
language
English
LU publication?
yes
id
19e2a293-574a-4411-924f-b46f42b39d98
date added to LUP
2018-04-10 12:38:14
date last changed
2018-10-03 12:04:19
@article{19e2a293-574a-4411-924f-b46f42b39d98,
  abstract     = {<p>Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability.</p>},
  articleno    = {1009},
  author       = {Yang, Yang and Urolagin, Siddhaling and Niroula, Abhishek and Ding, Xuesong and Shen, Bairong and Vihinen, Mauno},
  issn         = {1661-6596},
  keyword      = {Benchmark quality,Machine learning method,Mutation,Protein stability prediction,Variation interpretation},
  language     = {eng},
  month        = {04},
  number       = {4},
  publisher    = {MOLECULAR DIVERSITY PRESERVATION INT},
  series       = {International Journal of Molecular Sciences},
  title        = {Pon-tstab : Protein variant stability predictor. importance of training data quality},
  url          = {http://dx.doi.org/10.3390/ijms19041009},
  volume       = {19},
  year         = {2018},
}