ArticleThe FEBS journal2009
Investigation and prediction of the severity of p53 mutants using parameters from structural calculations.
Article in The FEBS journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Structural dynamics is a determinant of the functional significance of missense variants.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Recommended Guidelines for Validation, Quality Control, and Reporting ofCancer research · 2017Review
- Harmful somatic amino acid substitutions affect key pathways in cancers.BMC medical genomics · 2015Article
- Deriving a mutation index of carcinogenicity using protein structure and protein interfaces.PloS one · 2014Article
- Clinical relevance of gain-of-function mutations of p53 in high-grade serous ovarian carcinoma.PloS one · 2013Article
- Real value prediction of protein folding rate change upon point mutation.Journal of computer-aided molecular design · 2012Article
- Structural and biochemical investigation of two Arabidopsis shikimate kinases: the heat-inducible isoform is thermostable.Protein science : a publication of the Protein Society · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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