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:
https://lup.lub.lu.se/record/3635202
- author
- Shen, Bairong
; Bai, Jinwei
and Vihinen, Mauno
LU
- publishing date
- 2008
- 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
- pmid:18096555
- 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
- 2016-04-04 09:32:33
- date last changed
- 2022-04-08 03:30:55
@article{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}},
keywords = {{Amino Acid Substitution: genetics; Proteins: chemistry; Proteins: genetics}},
language = {{eng}},
number = {{1}},
pages = {{37--44}},
publisher = {{Oxford University Press}},
series = {{Protein Engineering Design & Selection}},
title = {{Physicochemical feature-based classification of amino acid mutations.}},
url = {{http://dx.doi.org/10.1093/protein/gzm084}},
doi = {{10.1093/protein/gzm084}},
volume = {{21}},
year = {{2008}},
}