Communication: Capturing protein multiscale thermal fluctuations
Date of Issue2015
School of Physical and Mathematical Sciences
Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to deliver accurate predictions for some large macromolecules on which GNM fails to produce accurate predictions. Finally, for a protein of N residues, mFRI is of linear scaling ( O(N) ) in computational complexity, in contrast to the order of O(N^3) for GNM.
The Journal of Chemical Physics
© 2015 American Institute of Physics. This paper was published in The Journal of Chemical Physics and is made available as an electronic reprint (preprint) with permission of American Institute of Physics. The published version is available at: [http://dx.doi.org/10.1063/1.4922045]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.