title: Communication: Capturing protein multiscale thermal fluctuations.
abstract: 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.
authors.affiliation: Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA.
authors.forename: Kristopher
authors.initials: K
authors.lastname: Opron
authors.affiliation: Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, USA.
authors.forename: Kelin
authors.initials: K
authors.lastname: Xia
authors.affiliation: Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA.
authors.forename: Guo-Wei
authors.initials: GW
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry*; D013696:Temperature
chemical_list: D011506:Proteins