Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data - PubMed (original) (raw)

Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data

Dmitry A Kondrashov et al. Biophys J. 2006.

Abstract

Simple coarse-grained models, such as the Gaussian network model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution (<or=1.0 A) x-ray crystal structures to optimize the interaction parameters. The average correlation between Gaussian network-model fluctuation predictions and the B-factors is 0.64 for the data set, consistent with a previous large-scale study. By separating residue interactions into covalent and noncovalent, we achieve an average correlation of 0.74, and addition of ligands and cofactors further improves the correlation to 0.75. However, further separating the noncovalent interactions into nonpolar, polar, and mixed yields no significant improvement. The addition of simple chemical information results in better prediction quality without increasing the size of the coarse-grained model.

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Figures

FIGURE 1

FIGURE 1

Cartoon of calmodulin structure (1EXR) in green with C_α_ atoms within 7.3 Å connected by magenta dotted lines to represent GNM interactions.

FIGURE 2

FIGURE 2

Contrast between residue interactions selected by C_α_ distance (magenta) and nearest-atom distance (blue). (A) Residues with a strong ring-stacking interaction with C_α_ distance >7.3 Å. (B) Residues not in chemical contact with C_α_ distance <7 Å. Both examples from sperm whale myoglobin structure (1A6M).

FIGURE 3

FIGURE 3

Examples of computed fluctuation profiles and experimental B-factors (normalized). (A) Worst prediction, 1J0P (0.46 CNM, 0.46 GNM). (B) Best prediction, 2BW4 (0.9 CNM, GNM 0.84).

FIGURE 4

FIGURE 4

Comparison of corresponding low-frequency modes from GNM and CNM. The upper curve shows the ratio of the eigenvalues divided by the lowest frequency, averaged over the 98 structures. The lower curve is the average dot product between the corresponding normal modes. Note the fast decline of the normal modes at higher frequencies.

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