Predicting the transactivation activity of p53 missense mutants using a four-body potential score derived from Delaunay tessellations.
We describe a novel statistical scoring method based on a computational geometry approach to predict the functional impact (transactivation activity) of missense mutations in the DNA-binding domain (DBD) of the tumor suppressor TP53, which is the most frequently mutated gene in human cancer. Residual scores (RS) for each residue were calculated to reflect differences in the compositional preferences of four nearest-neighbor residues between mutant and wild-type proteins. The RS were then combined into a residual score profile (RSP) representing the RS values for all 194 residues in the DBD. Mutants were grouped into functional categories based on their transactivation activities experimentally measured in yeast functional assays using p53-response elements from eight different promoters. While these functional categories showed significant differences in average RS, the latter lacked resolution power to predict the transactivation activities of individual mutants. In contrast, using decision tree models, we found that the RSP predicted transactivation with an accuracy varying between 64.2% and 78.5% depending on the promoter. Lastly, we used the best model to predict the functional outcome of all missense mutants in the DBD of p53 and compared the predictions with their frequency of occurrence in human cancers. We found that mutants predicted as functional (F) accounted for approximately 14% of all missense mutants found in cancers, while mutants predicted as nonfunctional (NF) represented approximately 86% of the mutants. These results show that this computational approach provides a fast and reliable method for predicting the functional impact of p53 mutants associated with cancer.
Mathe
Ewy
E
Department of Bioinformatics and Computational Biology, George Mason University, Manassas, Virginia, USA.
Olivier
Magali
M
Kato
Shunsuke
S
Ishioka
Chikashi
C
Vaisman
Iosif
I
Hainaut
Pierre
P
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016158:Genes, p53; D006801:Humans; D015233:Models, Statistical; D009154:Mutation; D020125:Mutation, Missense; D011401:Promoter Regions, Genetic; D012372:ROC Curve; D015533:Transcriptional Activation; D016159:Tumor Suppressor Protein p53 / Q000235:genetics*
D016159:Tumor Suppressor Protein p53