Campus Units

Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Mathematics, Bioinformatics and Computational Biology

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-2013

Journal or Book Title

Bulletin of Mathematical Biology

Volume

75

Issue

1

First Page

124

Last Page

160

DOI

10.1007/s11538-012-9797-y

Abstract

We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms Cα, C, N, and Cβ are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.

Comments

This is a manuscript of an article published as Park, Jun-Koo, Robert Jernigan, and Zhijun Wu. "Coarse grained normal mode analysis vs. refined Gaussian network model for protein residue-level structural fluctuations." Bulletin of mathematical biology 75, no. 1 (2013): 124-160. The final publication is available at link.springer.com via http://dx.doi.org/10.1007/s11538-012-9797-y. Posted with permission.

Copyright Owner

Society for Mathematical Biology

Language

en

File Format

application/pdf

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