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    Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action

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    Author
    Gurunathan, Vaibhav
    Hamre, John
    Klimov, Dmitri K
    Jafri, Mohsin Saleet
    Date
    2021-10-19
    Journal
    Biomolecules
    Publisher
    MDPI AG
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3390/biom11101541
    Abstract
    Alzheimer’s disease, the most common form of dementia, currently has no cure. There are only temporary treatments that reduce symptoms and the progression of the disease. Alzheimer’s disease is characterized by the prevalence of plaques of aggregated amyloid β (Aβ) peptide. Recent treatments to prevent plaque formation have provided little to relieve disease symptoms. Although there have been numerous molecular simulation studies on the mechanisms of Aβ aggregation, the signaling role has been less studied. In this study, a total of over 38,000 simulated structures, generated from molecular dynamics (MD) simulations, exploring different conformations of the Aβ42 mutants and wild-type peptides were used to examine the relationship between Aβ torsion angles and disease measures. Unique methods characterized the data set and pinpointed residues that were associated in aggregation and others associated with signaling. Machine learning techniques were applied to characterize the molecular simulation data and classify how much each residue influenced the predicted variant of Alzheimer’s Disease. Orange3 data mining software provided the ability to use these techniques to generate tables and rank the data. The test and score module coupled with the confusion matrix module analyzed data with calculations of specificity and sensi-tivity. These methods evaluating frequency and rank allowed us to analyze and predict important residues associated with different phenotypic measures. This research has the potential to help un-derstand which specific residues of Aβ should be targeted for drug development. © 2021 by the authors.
    Keyword
    Alzheimer’s disease
    amyloid beta
    cerebral amyloid angiopathy
    machine learning
    molecular simulation
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/16948
    ae974a485f413a2113503eed53cd6c53
    10.3390/biom11101541
    Scopus Count
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