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In silico modeling of pathogenic point mutations in APP, PSEN1 and PSEN2 in Asia
Author(s) -
Nguyen Thuy Trang,
Vo Tuong Kha,
Vo Van Giau
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.037111
Subject(s) - psen1 , presenilin , missense mutation , in silico , genetics , biology , mutation , point mutation , gene , dementia , disease , alzheimer's disease , medicine , pathology
Background Alzheimer’s disease (AD), the most common form of senile dementia, is a genetically complex disorder. In most Asian countries, the population and the number of AD patients are growing rapidly, and the genetics of AD has been extensively studied. Amyloid protein precursor ( APP ), presenilin‐1 ( PSEN1 ), and presenilin‐2 ( PSEN2 ) mutations cause autosomal dominant forms of early‐onset Alzheimer disease (EOAD). Although these genes were identified in the last several decades, variant classification remains a challenge, highlighting the need to elucidate their role in neuropathologic features of autosomal dominant ADAD. Method We tested the performance of four prediction tools (Align‐GVGD, SIFT, PolyPhen‐2, MutationTaster2) using a set of 50 missense variants that had previously been reported. Finally, 3‐D modeling was also performed for all mutations. Result We summarized substitution mutations in the APP, PSEN1 and PSEN2 genes in Asia and attempted to identify pathogenic mutations using Align‐GVGD, SIFT, PolyPhen‐2, MutationTaster2, and 3‐D structure analysis techniques. Most of the mutations were confirmed as pathogenic mutations by these algorithms. Conclusion Our results contribute to a better understanding of the behavior of the mutations and their pathogenic nature process, which is necessary to understand AD pathogenesis. Following this work, some potential mutations would be chosen and further checked out their pathogenicity of in AD through cellular studies.