Biology – Quantitative Biology – Biomolecules
Scientific paper
2006-04-06
Biophysical Journal, vol 91 (8), 2006
Biology
Quantitative Biology
Biomolecules
18 pages, 4 figures, 1 supplemental file (cnm_si.tex)
Scientific paper
10.1529/biophysj.106.085894
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 X-ray crystal structures to optimize the interaction parameters. The average correlation between GNM 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.
Cui Qiang
Kondrashov Dmitry A.
Phillips George N. Jr.
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