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

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 prev...

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Autores principales: Gurunathan, Vaibhav, Hamre, John, Klimov, Dmitri K., Jafri, Mohsin Saleet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534076/
https://www.ncbi.nlm.nih.gov/pubmed/34680174
http://dx.doi.org/10.3390/biom11101541
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author Gurunathan, Vaibhav
Hamre, John
Klimov, Dmitri K.
Jafri, Mohsin Saleet
author_facet Gurunathan, Vaibhav
Hamre, John
Klimov, Dmitri K.
Jafri, Mohsin Saleet
author_sort Gurunathan, Vaibhav
collection PubMed
description 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 sensitivity. 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 understand which specific residues of Aβ should be targeted for drug development.
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spelling pubmed-85340762021-10-23 Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action Gurunathan, Vaibhav Hamre, John Klimov, Dmitri K. Jafri, Mohsin Saleet Biomolecules Article 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 sensitivity. 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 understand which specific residues of Aβ should be targeted for drug development. MDPI 2021-10-19 /pmc/articles/PMC8534076/ /pubmed/34680174 http://dx.doi.org/10.3390/biom11101541 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gurunathan, Vaibhav
Hamre, John
Klimov, Dmitri K.
Jafri, Mohsin Saleet
Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title_full Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title_fullStr Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title_full_unstemmed Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title_short Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
title_sort data mining of molecular simulations suggest key amino acid residues for aggregation, signaling and drug action
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534076/
https://www.ncbi.nlm.nih.gov/pubmed/34680174
http://dx.doi.org/10.3390/biom11101541
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