Computational approach towards identification of pathogenic missense mutations in AMELX gene and their possible association with amelogenesis imperfecta

Document Type : Original article

Authors

1 Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India

2 Department of Microbiology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, 162, Poonamallee High Road, Chennai 600077, Tamil Nadu, India

3 Biomedical Research Unit and Laboratory Animal Centre-Dental Research Cell, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai 600077, India

Abstract

Amelogenin gene (AMEL-X) encodes an enamel protein called amelogenin, which plays a vital role in tooth development. Any mutations in this gene or the associated pathway lead to developmental abnormalities of the tooth. The present study aims to analyze functional missense mutations in AMEL-X genes and derive an association with amelogenesis imperfecta. The information on missense mutations of human AMEL-X gene was collected from Ensembl database (https://asia.ensembl.org). Three different computational tools viz., SIFT, PolyPhen and PROVEAN were used to identify the deleterious or pathogenic forms of mutations in the gene studied. I-Mutant Suit was used to identify the stability of the proteins identified as deleterious by the three tools. Further, MutPred analysis revealed the pathogenicity of these mutations. Among 96 missense variants reported in AMEL-X gene, 18 were found to be deleterious using the three prediction tools (SIFT, PolyPhen and PROVEAN). When these variants were subjected to protein stability analysis, about 14 missense variants showed decreased stability whereas the other 8 variants showed increased stability. Further, these variants were analyzed using MutPred which identified 9 variants to be highly pathogenic. ExAC database revealed that all the pathogenic mutations had a minor allele frequency less than 0.01. The in silico analysis revealed highly pathogenic mutations in amelogenin gene which could have a putative association with amelogenesis imperfecta. These mutations should be screened in patients for early diagnosis of susceptibility to AI.

Keywords

References

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