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

Research output: Contribution to journalArticle

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, 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.

Details

Authors
Organisations
External organisations
  • Soochow University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Biochemistry and Molecular Biology
  • Bioinformatics and Systems Biology

Keywords

  • Benchmark quality, Machine learning method, Mutation, Protein stability prediction, Variation interpretation
Original languageEnglish
Article number1009
JournalInternational Journal of Molecular Sciences
Volume19
Issue number4
Publication statusPublished - 2018 Apr 1
Publication categoryResearch
Peer-reviewedYes