2021
DOI: 10.1080/07391102.2021.1871955
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Insights in the structural understanding of amyloidogenicity and mutation-led conformational dynamics of amyloid beta (Aβ) through molecular dynamics simulations and principal component analysis

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Cited by 9 publications
(8 citation statements)
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“…PCA was computed on the backbone (Cα) atoms of the amino acid residues to assess the structural alterations or conformational changes present in the secondary structure of HSA in the presence and absence of the dye. PCA serves as a potent analytical tool, facilitating the visualization of synchronized atomic displacements occurring within macromolecules during MD trajectories 50,65–68 . Significantly, the initial three Eigenvectors collectively elucidated around 60% of the dynamic motions 50–52 .…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…PCA was computed on the backbone (Cα) atoms of the amino acid residues to assess the structural alterations or conformational changes present in the secondary structure of HSA in the presence and absence of the dye. PCA serves as a potent analytical tool, facilitating the visualization of synchronized atomic displacements occurring within macromolecules during MD trajectories 50,65–68 . Significantly, the initial three Eigenvectors collectively elucidated around 60% of the dynamic motions 50–52 .…”
Section: Resultsmentioning
confidence: 99%
“…PCA serves as a potent analytical tool, facilitating the visualization of synchronized atomic displacements occurring within macromolecules during MD trajectories. 50,[65][66][67][68] Significantly, the initial three Eigenvectors collectively elucidated around 60% of the dynamic motions. [50][51][52] The overlay of primary principal components on a 2D plane provides a lucid representation of atomic displacement patterns (Figure 10).…”
Section: Principal Component Analysismentioning
confidence: 99%
“…Principal component analysis (PCA) was performed in molecular dynamics simulation (MD simulation), in which principle components (PCs) are eigenvectors that specify the motion's direction and eigenvalues specify the amount of residual motion. The PCA was conducted using Gromacs 2021.5 for the complexes constructed by multi-epitope vaccines with B7-1, B7-2, TLR-2, TLR-4, and projected PC1 and PC2 into two dimensions 51 .…”
Section: Methodsmentioning
confidence: 99%
“…PCA can identify important patterns of interacting systems generated by MD simulations. In MD, the principal components (PCs) are the eigenvectors that determine the direction of motion, and the eigenvalues represent the degree of residual motion 49 . In this study, the Gromacs 2021.4 was used to analyze the trajectory of the simulation, and the most critical principal component 1 (PC1) and principal component 2 (PC2) were projected into 2 dimensions.…”
Section: Methodsmentioning
confidence: 99%