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

Autor:	Usmanova DR; Department of Systems Biology, Columbia University Medical Center, New York, NY, USA., Bogatyreva NS; Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Spain.; Universitat Pompeu Fabra (UPF), Barcelona, Spain.; Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, Pushchino, Moscow Region, Russia., Ariño Bernad J; Centre de Formació Interdisciplinària Superior, Universitat Politècnica de Catalunya, Barcelona, Spain., Eremina AA; School of Biological Sciences, College of Science and Engineering, University of Edinburgh, Edinburgh, UK., Gorshkova AA; Biological Faculty, Lomonosov Moscow State University, Moscow, Russia., Kanevskiy GM; Higher Chemical College of the Russian Academy of Sciences, Moscow, Russia., Lonishin LR; Faculty of Technical Physics, Institute of Physics, Nanotechnology and Telecommunications, Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russia., Meister AV; Department of Medicine, Novosibirsk State University, Novosibirsk, Russia., Yakupova AG; Biological Faculty, Lomonosov Moscow State University, Moscow, Russia., Kondrashov FA; Institute of Science and Technology, Klosterneuburg, Austria., Ivankov DN; Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, Pushchino, Moscow Region, Russia.; Institute of Science and Technology, Klosterneuburg, Austria.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2018 Nov 01; Vol. 34 (21), pp. 3653-3658.
DOI:	10.1093/bioinformatics/bty340
Abstrakt:	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. Availability and Implementation: All calculations were implemented by in-house PERL scripts. Supplementary Information: Supplementary data are available at Bioinformatics online. Note: The article 10.1093/bioinformatics/bty348, published alongside this paper, also addresses the problem of biases in protein stability change predictions.
Databáze:	MEDLINE
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