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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. | Investigation and prediction of the severity of p53 mutants using parameters from structural calculations Jonas Carlsson1 Thierry Soussi2 3 and Bengt Persson1 4 1 IFM Bioinformatics Linkoping University Sweden 2 Department of Oncology-Pathology Cancer Center Karolinska CCK Karolinska Institutet Stockholm Sweden 3 Universite Pierre et Marie Curie-Paris6 France 4 Department of Celland Molecular Biology Karolinska Institutet Stockholm Sweden Keywords cancer molecular modelling mutations p53 structuralprediction Correspondence J. Carlsson Department of Physics Chemistry and Biology IFM Bioinformatics Linkoping University SE-581 83 Linkoping Sweden Fax 4613137568 Tel 4613282423 E-mail jonca@ifm.liu.se Re-use of this article is permitted in accordance with the Terms and Conditions set out at http www3.interscience. wiley.com authorresources onlineopen.html 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. Received 23 December 2008 revised 3 April2009 accepted 29 May 2009 doi 10.1111 j.1742-4658.2009.07124.x Introduction Recently several large-scale screens for genetic alterations in human cancers have been published 1 2 . The identification
