Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

• Main Authors: Xia, Kelin, Opron, Kristopher, Wei, Guo-Wei
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2016
• Subjects: Proteins; Correlation functions
• Online Access: https://hdl.handle.net/10356/82115; http://hdl.handle.net/10220/41118
• Institution: Nanyang Technological University

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.