Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods

By Ewy A. Mathé, Magali Olivier, S. Kato, Chikashi Ishioka, Pierre Hainaut, Sean V. Tavtigian. Nucleic Acids Research, 2006

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Prediction of the biological effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the ‘biochemical distance’ of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation (∼88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at .

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/b03ff0603e8b4cc5cdb5e5bdf6df5fc41fc8f7a8

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Citations in the corpus

Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database (2007, methodology): “fr]) based on interspecies sequence conservation [Mathe et al., 2006].”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, background): “high rates of false-positive and false-negative predictions [152, 153].”
TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data (2016, methodology): “, 2009], AGVGD [Mathe et al., 2006], Polyphen [Adzhubei et al.”
Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed (2011, methodology): “Several studies have compared the prediction accuracy of sequence [Balasubramanian et al., 2005; Mathe et al., 2006] and structure-based algorithms [Bao and Cui, 2005; Chan et al., 2007; Chao et al., 2008] using alignments generated by the algorithms or manually curated alignments.”