Computing the Pathogenicity of Alzheimer’s Disease Presenilin 1 Mutations

Ning Tang, Budheswar Dehury, Kasper Planeta Kepp*

*Corresponding author for this work

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Alzheimer’s disease (AD) is one of the major global health challenges of the 21st Century. More than 200 distinct mutations in presenilin 1 (PSEN1) cause severe early-onset familial AD (FAD), and are thus of central interest to the etiology of AD. PSEN1 is the catalytic subunit of γ-secretase which produces Aβ, and the mutations tend to increase the produced Aβ42/Aβ40 ratio. The molecular reasons for the pathogenesis of these mutations are unknown. We studied a close-to complete dataset of PSEN1 mutations using 21 different computational methods hypothesized  to reproduce pathogenesis, using both sequence- and structure-based methods with the full γ secretase complex as input. First, we tested if pathogenicity can be estimated accurately using  all possible mutations in PSEN1 as a direct control. Several methods predict the pathogenicity  of the mutations (pathogenic vs. all other possible mutations) well, with accuracies approaching  90%. We then designed a stricter test for predicting the severity of the mutations estimated by  the average clinical age of symptom onset for mutation carriers. Surprisingly, we can predict  clinical age of symptom onset at 95% confidence or higher with several methods. Accordingly,  our results show that simple biochemical properties of the amino acid changes rationalize an important part of the pathogenicity of FAD-causing PSEN1 mutations. Although pathogenic mutations generally destabilize γ-secretase, all tested protein stability methods failed to predict pathogenicity. Thus, either the static cryo-electron microscopy-derived molecular dynamics equilibrated structures used as input fail to capture the stability effect of mutated side chains, or protein stability is simply not a key factor in the pathogenicity. Our findings suggest that the chemical causes of FAD may be modelled and lend promise to the development of a semi quantitative model predicting the age of onset of mutation carriers that could eventually become of care-strategic value.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Issue number2
Pages (from-to)858-870
Number of pages13
Publication statusPublished - 2019


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