Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

Dinara R. Usmanova, Natalya S. Bogatyreva, Joan Ariño Bernad, Aleksandra A. Eremina, Anastasiya A. Gorshkova, German M. Kanevskiy, Lyubov R. Lonishin, Alexander V. Meister, Alisa G. Yakupova, Fyodor A. Kondrashov, Dmitry N. Ivankov

Результат исследования: Научные публикации в периодических изданияхстатья

10 Цитирования (Scopus)

Аннотация

Motivation: Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations. Results: Here we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here.

Язык оригиналаанглийский
Страницы (с-по)3653-3658
Число страниц6
ЖурналBioinformatics
Том34
Номер выпуска21
DOI
СостояниеОпубликовано - 1 янв 2018

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  • Цитировать

    Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., & Ivankov, D. N. (2018). Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics, 34(21), 3653-3658. https://doi.org/10.1093/bioinformatics/bty340