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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Soochow University
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Biokemi och molekylärbiologi
  • Bioinformatik och systembiologi

Nyckelord

Originalspråkengelska
Artikelnummer1009
TidskriftInternational Journal of Molecular Sciences
Volym19
Utgivningsnummer4
StatusPublished - 2018 apr 1
PublikationskategoriForskning
Peer review utfördJa