Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82115
Title: Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)
Authors: Xia, Kelin
Opron, Kristopher
Wei, Guo-Wei
Keywords: Proteins
Correlation functions
Issue Date: 2015
Source: Xia, K., Opron, K., & Wei, G.-W. (2015). Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM). The Journal of Chemical Physics, 143(20), 204106-.
Series/Report no.: The Journal of Chemical Physics
Abstract: Gaussian networkmodel (GNM) and anisotropicnetworkmodel (ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM (gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method. With this connection, we further introduce two multiscale elasticnetworkmodels, namely, multiscale GNM (mGNM) and multiscale ANM (mANM), which are able to incorporate different scales into the generalized Kirchhoff matrices or generalized Hessian matrices. We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins. We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. More importantly, we reveal intrinsic multiscale behavior in proteinstructures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate the benefits of our mGNM through the B-factor predictions of many proteins that fail the original GNM method. We show that the proposed mGNM can also be used to analyzeprotein domain separations. Finally, we showcase the ability of our mANM for the analysis of protein collective motions.
URI: https://hdl.handle.net/10356/82115
http://hdl.handle.net/10220/41118
ISSN: 0021-9606
DOI: 10.1063/1.4936132
Rights: © 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/10.1063/1.4936132]. 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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