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Physicochemical feature-based classification of amino acid mutations.

Shen, Bairong; Bai, Jinwei and Vihinen, Mauno LU (2008) In Protein Engineering Design & Selection 21(1). p.37-44
Abstract
A huge quantity of gene and protein sequences have become available during the post-genomic era, and information about genetic variations, including amino acid substitutions and SNPs, is accumulating rapidly. To understand the effects of these changes, it is often essential to apply bioinformatics tools. Where there is a lack of homologous sequences or a three-dimensional structure, it becomes essential to predict the effects of mutations based solely on protein sequence information. Several computational methods utilizing machine learning techniques have been developed. These predictions generally use the 20-alphabet amino acid code to train the model. With limited available data, the 20-alphabet amino acid features may introduce so many... (More)
A huge quantity of gene and protein sequences have become available during the post-genomic era, and information about genetic variations, including amino acid substitutions and SNPs, is accumulating rapidly. To understand the effects of these changes, it is often essential to apply bioinformatics tools. Where there is a lack of homologous sequences or a three-dimensional structure, it becomes essential to predict the effects of mutations based solely on protein sequence information. Several computational methods utilizing machine learning techniques have been developed. These predictions generally use the 20-alphabet amino acid code to train the model. With limited available data, the 20-alphabet amino acid features may introduce so many parameters that the model becomes over-fitted. To decrease the number of parameters, we propose a physicochemical feature-based method to forecast the effects of amino acid substitutions on protein stability. Protein structure alterations caused by mutations can be classified as stabilizing or destabilizing. Based on experimental folding-unfolding free energy (DeltaDeltaG) values, we trained a support vector machine with a cleaned data set. The physicochemical properties of the mutated residues, the number of neighboring residues in the primary sequence and the temperature and pH were used as input attributes. Different kernel functions, attributes and window sizes were optimized. An average accuracy of 80% was obtained in cross-validation experiments. (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Amino Acid Substitution: genetics, Proteins: chemistry, Proteins: genetics
in
Protein Engineering Design & Selection
volume
21
issue
1
pages
37 - 44
publisher
Oxford University Press
external identifiers
  • PMID:18096555
  • Scopus:38349113850
ISSN
1741-0126
DOI
10.1093/protein/gzm084
language
English
LU publication?
no
id
dcab4f47-6c10-4cda-bcf7-a6d85b56fc68 (old id 3635202)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/18096555?dopt=Abstract
date added to LUP
2013-06-12 16:29:38
date last changed
2016-10-13 04:32:28
@misc{dcab4f47-6c10-4cda-bcf7-a6d85b56fc68,
  abstract     = {A huge quantity of gene and protein sequences have become available during the post-genomic era, and information about genetic variations, including amino acid substitutions and SNPs, is accumulating rapidly. To understand the effects of these changes, it is often essential to apply bioinformatics tools. Where there is a lack of homologous sequences or a three-dimensional structure, it becomes essential to predict the effects of mutations based solely on protein sequence information. Several computational methods utilizing machine learning techniques have been developed. These predictions generally use the 20-alphabet amino acid code to train the model. With limited available data, the 20-alphabet amino acid features may introduce so many parameters that the model becomes over-fitted. To decrease the number of parameters, we propose a physicochemical feature-based method to forecast the effects of amino acid substitutions on protein stability. Protein structure alterations caused by mutations can be classified as stabilizing or destabilizing. Based on experimental folding-unfolding free energy (DeltaDeltaG) values, we trained a support vector machine with a cleaned data set. The physicochemical properties of the mutated residues, the number of neighboring residues in the primary sequence and the temperature and pH were used as input attributes. Different kernel functions, attributes and window sizes were optimized. An average accuracy of 80% was obtained in cross-validation experiments.},
  author       = {Shen, Bairong and Bai, Jinwei and Vihinen, Mauno},
  issn         = {1741-0126},
  keyword      = {Amino Acid Substitution: genetics,Proteins: chemistry,Proteins: genetics},
  language     = {eng},
  number       = {1},
  pages        = {37--44},
  publisher    = {ARRAY(0x95fc840)},
  series       = {Protein Engineering Design & Selection},
  title        = {Physicochemical feature-based classification of amino acid mutations.},
  url          = {http://dx.doi.org/10.1093/protein/gzm084},
  volume       = {21},
  year         = {2008},
}