Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling...
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| Format: | Online |
| Language: | English |
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Blackwell Publishing Ltd
2009
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730554/ |
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pubmed-2730554 |
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pubmed-27305542009-08-27 Investigation and prediction of the severity of p53 mutants using parameters from structural calculations Carlsson, Jonas Soussi, Thierry Persson, Bengt Original Articles A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers. Blackwell Publishing Ltd 2009-08 /pmc/articles/PMC2730554/ /pubmed/19558493 http://dx.doi.org/10.1111/j.1742-4658.2009.07124.x Text en Journal compilation © 2009 Federation of European Biochemical Societies http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Carlsson, Jonas Soussi, Thierry Persson, Bengt |
| spellingShingle |
Carlsson, Jonas Soussi, Thierry Persson, Bengt Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| author_facet |
Carlsson, Jonas Soussi, Thierry Persson, Bengt |
| author_sort |
Carlsson, Jonas |
| title |
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| title_short |
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| title_full |
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| title_fullStr |
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| title_full_unstemmed |
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| title_sort |
investigation and prediction of the severity of p53 mutants using parameters from structural calculations |
| description |
A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers. |
| publisher |
Blackwell Publishing Ltd |
| publishDate |
2009 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730554/ |
| _version_ |
1611442547218972672 |