IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002646.html
   My bibliography  Save this article

The Landscape of the Prion Protein's Structural Response to Mutation Revealed by Principal Component Analysis of Multiple NMR Ensembles

Author

Listed:
  • Deena M A Gendoo
  • Paul M Harrison

Abstract

Prion Proteins (PrP) are among a small number of proteins for which large numbers of NMR ensembles have been resolved for sequence mutants and diverse species. Here, we perform a comprehensive principle components analysis (PCA) on the tertiary structures of PrP globular proteins to discern PrP subdomains that exhibit conformational change in response to point mutations and clade-specific evolutionary sequence mutation trends. This is to our knowledge the first such large-scale analysis of multiple NMR ensembles of protein structures, and the first study of its kind for PrPs. We conducted PCA on human (n = 11), mouse (n = 14), and wildtype (n = 21) sets of PrP globular structures, from which we identified five conformationally variable subdomains within PrP. PCA shows that different non-local patterns and rankings of variable subdomains arise for different pathogenic mutants. These subdomains may thus be key areas for initiating PrP conversion during disease. Furthermore, we have observed the conformational clustering of divergent TSE-non-susceptible species pairs; these non-phylogenetic clusterings indicate structural solutions towards TSE resistance that do not necessarily coincide with evolutionary divergence. We discuss the novelty of our approach and the importance of PrP subdomains in structural conversion during disease. Author Summary: Prion Proteins (PrP) cause a variety of incurable TSE diseases, and are among a small number of proteins for which large numbers of NMR ensembles have been resolved for sequence mutants and diverse species. Here, we perform a comprehensive PCA study to assess conformational variation and discern the landscape of the PrP structural response to sequence mutation. This is to our knowledge the first large-scale analysis of multiple NMR ensembles for a specific protein, and the first study to perform a multivariate PCA on the native globular structures of PrP. We conducted exhaustive PCA on three PrP subsets: human and mouse subsets that include structures of sequence mutants, and the set of wild-type PrP (16 PrP species). PCA shows that different non-local patterns of variable subdomains arise for different pathogenic mutants. These subdomains may thus be key areas for initiating PrP conversion during disease. Furthermore, we observed that some evolutionarily divergent species that are non-susceptible to TSEs have surprising structural similarities in their PrPs. We discuss the novelty of our approach with respect to prions, and the advantage of this analysis as a fast, reliable starting point to identify interesting domains that may warrant further experimental and computational analysis.

Suggested Citation

  • Deena M A Gendoo & Paul M Harrison, 2012. "The Landscape of the Prion Protein's Structural Response to Mutation Revealed by Principal Component Analysis of Multiple NMR Ensembles," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-14, August.
  • Handle: RePEc:plo:pcbi00:1002646
    DOI: 10.1371/journal.pcbi.1002646
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002646
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002646&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002646?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    2. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    3. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    4. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    5. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    7. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    8. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    9. Cling, Jean-Pierre & Delecourt, Clément, 2022. "Interlinkages between the Sustainable Development Goals," World Development Perspectives, Elsevier, vol. 25(C).
    10. Hino, Hideitsu & Wakayama, Keigo & Murata, Noboru, 2013. "Entropy-based sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 105-114.
    11. Angelucci, Federica & Conforti, Piero, 2010. "Risk management and finance along value chains of Small Island Developing States. Evidence from the Caribbean and the Pacific," Food Policy, Elsevier, vol. 35(6), pages 565-575, December.
    12. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    13. Taner Akan & Tim Solle, 2022. "Do macroeconomic and financial governance matter? Evidence from Germany, 1950–2019," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 993-1045, October.
    14. Paolo Rizzi & Paola Graziano & Antonio Dallara, 2018. "A capacity approach to territorial resilience: the case of European regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(2), pages 285-328, March.
    15. Pérez, Claudia & Claveria, Oscar, 2020. "Natural resources and human development: Evidence from mineral-dependent African countries using exploratory graphical analysis," Resources Policy, Elsevier, vol. 65(C).
    16. Zeynep Ozkok, 2015. "Financial openness and financial development: an analysis using indices," International Review of Applied Economics, Taylor & Francis Journals, vol. 29(5), pages 620-649, September.
    17. Asongu, Simplice A & Odhiambo, Nicholas M, 2019. "Governance,CO2 emissions and inclusive human development in Sub-Saharan Africa," Working Papers 25253, University of South Africa, Department of Economics.
    18. Anne M. Lausier & Shaleen Jain, 2018. "Diversity in global patterns of observed precipitation variability and change on river basin scales," Climatic Change, Springer, vol. 149(2), pages 261-275, July.
    19. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. Zarzo, Manuel & Martí, Pau, 2011. "Modeling the variability of solar radiation data among weather stations by means of principal components analysis," Applied Energy, Elsevier, vol. 88(8), pages 2775-2784, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1002646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.