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Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules

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  • Akio Kitao

    (School of Life Science and Technology, Tokyo Institute of Technology, Tokyo 152-8550, Japan)

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 have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods and their applications for investigating protein dynamics.

Suggested Citation

  • Akio Kitao, 2022. "Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules," J, MDPI, vol. 5(2), pages 1-20, June.
    • Handle: RePEc:gam:jjopen:v:5:y:2022:i:2:p:21-317:d:843553
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    References listed on IDEAS

    as
    1. Jan H Peters & Bert L de Groot, 2012. "Ubiquitin Dynamics in Complexes Reveal Molecular Recognition Mechanisms Beyond Induced Fit and Conformational Selection," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-10, October.
    2. A. Nodehi & M. Golalizadeh & A. Heydari, 2015. "Dihedral angles principal geodesic analysis using nonlinear statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 1962-1972, September.
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