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Going beyond Clustering in MD Trajectory Analysis: An Application to Villin Headpiece Folding

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  • Aruna Rajan
  • Peter L Freddolino
  • Klaus Schulten

Abstract

Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD) simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i) they are not data driven, (ii) they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii) they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA) and a non-metric multidimensional scaling (nMDS) method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous.

Suggested Citation

  • Aruna Rajan & Peter L Freddolino & Klaus Schulten, 2010. "Going beyond Clustering in MD Trajectory Analysis: An Application to Villin Headpiece Folding," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-12, April.
    • Handle: RePEc:plo:pone00:0009890
    DOI: 10.1371/journal.pone.0009890
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    References listed on IDEAS

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    1. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. II," Psychometrika, Springer;The Psychometric Society, vol. 27(3), pages 219-246, September.
    2. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    3. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. I," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 125-140, June.
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    1. Hamid Hadi-Alijanvand & Elizabeth A Proctor & Bahram Goliaei & Nikolay V Dokholyan & Ali A Moosavi-Movahedi, 2012. "Thermal Unfolding Pathway of PHD2 Catalytic Domain in Three Different PHD2 Species: Computational Approaches," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.

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