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2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop
Generalized spring tensor models for protein fluctuation dynamics and conformation changes
Washington, DC USA
November 01-November 04
ISBN: 978-1-4244-5121-0
| ASCII Text | x | ||
| Tu-Liang Lin, Guang Song, "Generalized spring tensor models for protein fluctuation dynamics and conformation changes," 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 136-143, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/BIBMW.2009.5332117, author = { Tu-Liang Lin and Guang Song}, title = {Generalized spring tensor models for protein fluctuation dynamics and conformation changes}, journal ={2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops}, volume = {0}, year = {2009}, isbn = {978-1-4244-5121-0}, pages = {136-143}, doi = {http://doi.ieeecomputersociety.org/10.1109/BIBMW.2009.5332117}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops TI - Generalized spring tensor models for protein fluctuation dynamics and conformation changes SN - 978-1-4244-5121-0 SP136 EP143 A1 - Tu-Liang Lin, A1 - Guang Song, PY - 2009 KW - protein conformation changes KW - generalized spring tensor models KW - protein fluctuation dynamics KW - coarse-grained elastic network models KW - computer simulations KW - all-atom models KW - Gaussian network model KW - anisotropic network model VL - 0 JA - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops ER - | |||
In the last decade, various coarse-grained elastic network models have been developed to study the large-scale motions of proteins and protein complexes where computer simulations using detailed all-atom models are not feasible. Among these models, the Gaussian network model (GNM) and anisotropic network model (ANM) have been widely used. Both models have strengths and limitations. GNM can predict the relative magnitudes of the fluctuations well, but due to its isotropic assumption, it can not be applied to predict the directions of the fluctuations. In contrast, ANM adds the ability to do the latter, but it loses a significant amount of precision in the prediction of the magnitudes. In this article, we develop a generalized spring tensor model (STeM) that is able to predict well both the magnitudes and the directions of the fluctuations. STeM also outperforms ANM in explaining protein conformation changes. All of these are accomplished without sacrificing the essential features that have made ANM and GNM attractive.
Index Terms:
protein conformation changes, generalized spring tensor models, protein fluctuation dynamics, coarse-grained elastic network models, computer simulations, all-atom models, Gaussian network model, anisotropic network model
Citation:
Tu-Liang Lin, Guang Song, "Generalized spring tensor models for protein fluctuation dynamics and conformation changes," bibmw, pp.136-143, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009
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