2022
DOI: 10.3390/j5020021
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Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules

Abstract: Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high-dimensional space. PCA and many PCA-related methods… Show more

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Cited by 38 publications
(28 citation statements)
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“…In order to further understand the structural dynamics of the WT and E50A mutant picALuc, we utilized the dimensionality reduction analysis and performed principal component analysis (PCA) of their trajectories, which is known to reveal collective motions in proteins 45 using the PCA module available in the Python-based MDAnalysis package 46, 47 . Cumulative variance analysis of the principal components revealed the presence dominant collective motions in both the WT and the E50A mutant picALuc (Supplementary Figure 10) since principal components 1 and 2 (PC1 and PC2) could account for 69.8 and 19.2% of the variance in the WT protein while they could account for 74.2 and 19.8% of the variance in the E50A mutant protein.…”
Section: Resultsmentioning
confidence: 99%
“…In order to further understand the structural dynamics of the WT and E50A mutant picALuc, we utilized the dimensionality reduction analysis and performed principal component analysis (PCA) of their trajectories, which is known to reveal collective motions in proteins 45 using the PCA module available in the Python-based MDAnalysis package 46, 47 . Cumulative variance analysis of the principal components revealed the presence dominant collective motions in both the WT and the E50A mutant picALuc (Supplementary Figure 10) since principal components 1 and 2 (PC1 and PC2) could account for 69.8 and 19.2% of the variance in the WT protein while they could account for 74.2 and 19.8% of the variance in the E50A mutant protein.…”
Section: Resultsmentioning
confidence: 99%
“…This reduction in the data is derived by the linear transformation of the original variables to a set of new concerted variables, which allows one to interpret the features of the data set from only few "principal components". 27 The eigenvalues of the first 10 principal components (eigenvectors) for the ligand−ChAT complexes are shown in (Figure 11 and Figure S10). These analyses showed that the first four components explained most of the variations in the data.…”
Section: ■ Results and Discussionmentioning
confidence: 99%
“…PCA allows identifying the principal components that together explain the overall motions of the protein. This reduction in the data is derived by the linear transformation of the original variables to a set of new concerted variables, which allows one to interpret the features of the data set from only few “principal components” . The eigenvalues of the first 10 principal components (eigenvectors) for the ligand–ChAT complexes are shown in (Figure and Figure S10).…”
Section: Resultsmentioning
confidence: 99%
“…Other methods open to researchers for analysis of energy include principal component analysis (PC), direct vector analysis and correlations such as Pearson correlation coefficient or Pearson’s R values [ 81 , 82 ]. This latter is used for analysis of energies and motions, although any two components can be tested to see if they are correlated to each other.…”
Section: Analysis Of Membrane Molecular Dynamic Simulationsmentioning
confidence: 99%