A Model Comparison for Characterizing Protein Motions from Structure

Abstract

To understand the extent structure plays in determining protein dynamics, a comparative study is made using three computational models that characterize native state dynamics starting from known protein structures taken from four distinct SCOP classifications. A geometrical simulation [1] (FRODA) is performed based on an initial rigid cluster decomposition using FIRST [2], and the results are compared to the commonly employed elastic network model (ANM) and molecular dynamics (MD) simulations. The essential dynamics is quantified by a direct analysis of a mode subspace constructed from ANM and a principal component analysis (PCA) on both the FRODA and MD trajectories using root mean square inner (RMSIP) product and principal angles (PA). Relative subspace sizes and overlaps are visualized using the projection of displacement vectors on the model modes. Additionally, a mode subspace is constructed from PCA on an exemplar set of X-ray crystal structures in order to determine similarly with respect to the generated ensembles. Our quantitative analysis reveals there is significant overlap across the three model subspaces and the model independent subspace. The subspaces generated from all three models were found to have high overlap for all four SCOP classes of proteins investigated, although FRODA provided the most robust sampling of the native basin. These results indicate that structure is the key determinant for native state dynamics. This work is supported by NIH grant 1R21HL093531.[1] Wells S, Menor S, Hespenheide B, Thorpe MF: Constrained geometric simulation of diffusive motion in proteins. Phys Biol, 2:S127-S136 (2005).[2] Jacobs DJ, Rader A, Kuhn LA, Thorpe MF: Graph Theory Predictions of Protein Flexibility. Proteins, 44:150-65 (2001).