期刊
PLOS ONE
卷 5, 期 4, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0009890
关键词
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资金
- National Institutes of Health (NIH) [P41-RR05969]
- National Science Foundation (NSF) [PHY0822613]
- National Center for Supercomputing Applications [MCA93S028]
- University of Illinois at Urbana-Champaign (UIUC)
- Keio University through Yoshi Oono
- Department of Physics, UIUC
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.
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