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Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms
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Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms
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Journal Article
Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms
2018
Overview
Mutations in PITX2 have been implicated in several genetic disorders, particularly Axenfeld-Rieger syndrome. In order to determine the most reliable bioinformatics tools to assess the likely pathogenicity of PITX2 variants, the results of bioinformatics predictions were compared to the impact of variants on PITX2 structure and function. The MutPred, Provean, and PMUT bioinformatic tools were found to have the highest performance in predicting the pathogenicity effects of all 18 characterized missense variants in PITX2, all with sensitivity and specificity >93%. Applying these three programs to assess the likely pathogenicity of 13 previously uncharacterized PITX2 missense variants predicted 12/13 variants as deleterious, except A30V which was predicted as benign variant for all programs. Molecular modeling of the PITX2 homoedomain predicts that of the 31 known PITX2 variants, L54Q, F58L, V83F, V83L, W86C, W86S, and R91P alter PITX2's structure. In contrast, the remaining 24 variants are not predicted to change PITX2's structure. The results of molecular modeling, performed on all the PITX2 missense mutations located in the homeodomain, were compared with the findings of eight protein stability programs. CUPSAT was found to be the most reliable in predicting the effect of missense mutations on PITX2 stability. Our results showed that for PITX2, and likely other members of this homeodomain transcription factor family, MutPred, Provean, PMUT, molecular modeling, and CUPSAT can reliably be used to predict PITX2 missense variants pathogenicity.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Alleles
/ Computational Biology - methods
/ Genetics
/ Genomes
/ Homeobox
/ Homeodomain Proteins - chemistry
/ Homeodomain Proteins - genetics
/ Homeodomain Proteins - metabolism
/ Humans
/ Medicine and Health Sciences
/ Mutation
/ Research and Analysis Methods
/ Structure-Activity Relationship
/ Structure-function relationships
/ Studies
/ Transcription Factors - chemistry
