Performance of protein stability predictors.
Khan, Sofia and Vihinen, Mauno LU
(2010)
In Human Mutation
31(6).
p.675-684
- Abstract
- Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein-stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the... (More)
- Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein-stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, that is, in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), Dmutant, and FoldX were the most reliable predictors. The stability-center predictors performed with similar accuracy. However, at best, the predictions were only moderately accurate ( approximately 60%) and significantly better tools would be needed for routine analysis of mutation effects. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3634729
- author
- Khan, Sofia
and Vihinen, Mauno
LU
- publishing date
- 2010
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Computational Biology: methods, Proteins: chemistry, Proteins: genetics
- in
- Human Mutation
- volume
- 31
- issue
- 6
- pages
- 675 - 684
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:20232415
- scopus:77952706843
- pmid:20232415
- ISSN
- 1059-7794
- DOI
- 10.1002/humu.21242
- language
- English
- LU publication?
- no
- id
- 04c7acec-adac-4a74-b27d-d1e5ebfde36f (old id 3634729)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/20232415?dopt=Abstract
- date added to LUP
- 2016-04-04 08:25:39
- date last changed
- 2022-04-15 20:13:58
@article{04c7acec-adac-4a74-b27d-d1e5ebfde36f,
abstract = {{Stability is a fundamental property affecting function, activity, and regulation of biomolecules. Stability changes are often found for mutated proteins involved in diseases. Stability predictors computationally predict protein-stability changes caused by mutations. We performed a systematic analysis of 11 online stability predictors' performances. These predictors are CUPSAT, Dmutant, FoldX, I-Mutant2.0, two versions of I-Mutant3.0 (sequence and structure versions), MultiMutate, MUpro, SCide, Scpred, and SRide. As input, 1,784 single mutations found in 80 proteins were used, and these mutations did not include those used for training. The programs' performances were also assessed according to where the mutations were found in the proteins, that is, in secondary structures and on the surface or in the core of a protein, and according to protein structure type. The extents to which the mutations altered the occupied volumes at the residue sites and the charge interactions were also characterized. The predictions of all programs were in line with the experimental data. I-Mutant3.0 (utilizing structural information), Dmutant, and FoldX were the most reliable predictors. The stability-center predictors performed with similar accuracy. However, at best, the predictions were only moderately accurate ( approximately 60%) and significantly better tools would be needed for routine analysis of mutation effects.}},
author = {{Khan, Sofia and Vihinen, Mauno}},
issn = {{1059-7794}},
keywords = {{Computational Biology: methods; Proteins: chemistry; Proteins: genetics}},
language = {{eng}},
number = {{6}},
pages = {{675--684}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Human Mutation}},
title = {{Performance of protein stability predictors.}},
url = {{http://dx.doi.org/10.1002/humu.21242}},
doi = {{10.1002/humu.21242}},
volume = {{31}},
year = {{2010}},
}