PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality
AbstractSeveral 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. View Full-Text
- Supplementary File 1:
PDF-Document (PDF, 348 KB)
Share & Cite This Article
Yang, Y.; Urolagin, S.; Niroula, A.; Ding, X.; Shen, B.; Vihinen, M. PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality. Int. J. Mol. Sci. 2018, 19, 1009.
Yang Y, Urolagin S, Niroula A, Ding X, Shen B, Vihinen M. PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality. International Journal of Molecular Sciences. 2018; 19(4):1009.Chicago/Turabian Style
Yang, Yang; Urolagin, Siddhaling; Niroula, Abhishek; Ding, Xuesong; Shen, Bairong; Vihinen, Mauno. 2018. "PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality." Int. J. Mol. Sci. 19, no. 4: 1009.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.