Standard

Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. / Usmanova, Dinara R.; Bogatyreva, Natalya S.; Ariño Bernad, Joan et al.

In: Bioinformatics, Vol. 34, No. 21, 01.11.2018, p. 3653-3658.

Research output: Contribution to journalArticlepeer-review

Harvard

Usmanova, DR, Bogatyreva, NS, Ariño Bernad, J, Eremina, AA, Gorshkova, AA, Kanevskiy, GM, Lonishin, LR, Meister, AV, Yakupova, AG, Kondrashov, FA & Ivankov, DN 2018, 'Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation', Bioinformatics, vol. 34, no. 21, pp. 3653-3658. https://doi.org/10.1093/bioinformatics/bty340

APA

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

Vancouver

Usmanova DR, Bogatyreva NS, Ariño Bernad J, Eremina AA, Gorshkova AA, Kanevskiy GM et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics. 2018 Nov 1;34(21):3653-3658. doi: 10.1093/bioinformatics/bty340

Author

Usmanova, Dinara R. ; Bogatyreva, Natalya S. ; Ariño Bernad, Joan et al. / Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. In: Bioinformatics. 2018 ; Vol. 34, No. 21. pp. 3653-3658.

BibTeX

@article{f0e16551b16b46aeb98e99d59553e371,
title = "Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation",
abstract = "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.",
keywords = "MODIFIED FOLDX PROTOCOL, PROTEIN STABILITY, SEQUENCE, SERVER",
author = "Usmanova, {Dinara R.} and Bogatyreva, {Natalya S.} and {Ari{\~n}o Bernad}, Joan and Eremina, {Aleksandra A.} and Gorshkova, {Anastasiya A.} and Kanevskiy, {German M.} and Lonishin, {Lyubov R.} and Meister, {Alexander V.} and Yakupova, {Alisa G.} and Kondrashov, {Fyodor A.} and Ivankov, {Dmitry N.}",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press.",
year = "2018",
month = nov,
day = "1",
doi = "10.1093/bioinformatics/bty340",
language = "English",
volume = "34",
pages = "3653--3658",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "21",

}

RIS

TY - JOUR

T1 - Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation

AU - Usmanova, Dinara R.

AU - Bogatyreva, Natalya S.

AU - Ariño Bernad, Joan

AU - Eremina, Aleksandra A.

AU - Gorshkova, Anastasiya A.

AU - Kanevskiy, German M.

AU - Lonishin, Lyubov R.

AU - Meister, Alexander V.

AU - Yakupova, Alisa G.

AU - Kondrashov, Fyodor A.

AU - Ivankov, Dmitry N.

N1 - Publisher Copyright: © The Author(s) 2018. Published by Oxford University Press.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - 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.

AB - 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.

KW - MODIFIED FOLDX PROTOCOL

KW - PROTEIN STABILITY

KW - SEQUENCE

KW - SERVER

UR - http://www.scopus.com/inward/record.url?scp=85053142079&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bty340

DO - 10.1093/bioinformatics/bty340

M3 - Article

C2 - 29722803

AN - SCOPUS:85053142079

VL - 34

SP - 3653

EP - 3658

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 21

ER -

ID: 17248452