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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:
author
publishing date
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
external identifiers
  • pmid:20232415
  • scopus:77952706843
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
2013-06-12 20:41:43
date last changed
2018-05-29 12:18:41
@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},
  keyword      = {Computational Biology: methods,Proteins: chemistry,Proteins: genetics},
  language     = {eng},
  number       = {6},
  pages        = {675--684},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {Performance of protein stability predictors.},
  url          = {http://dx.doi.org/10.1002/humu.21242},
  volume       = {31},
  year         = {2010},
}