PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality.
Int J Mol Sci
; 19(4)2018 Mar 28.
Article
en En
| MEDLINE
| ID: mdl-29597263
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Proteínas
/
Modelos Moleculares
/
Bases de Datos de Proteínas
/
Aprendizaje Automático
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Int J Mol Sci
Año:
2018
Tipo del documento:
Article
País de afiliación:
China