Out Of One, Many: Exploiting Intrinsic Motions To Explore Protein Structure Spaces

Reconstructing the energy landscape of a protein holds the key to characterizing its structural dynamics and function [1]. While the disparate spatio-temporal scales spanned by the slow dynamics challenge reconstruction in wet and dry laboratories, computational efforts have had recent success on proteins where a wealth of experimentally-known structures can be exploited to extract modes of motion. In [2], the authors propose the SoPriM method that extracts principle components (PCs) and utilizes them as variables of the structure space of interest. Stochastic optimization is employed to sample the structure space and its associated energy landscape in the defined varible space. We refer to this algorithm as SoPriM-PCA and compare it here to SoPriM-NMA, which investigates whether the landscape can be reconstructed with knowledge of modes of motion (normal modes) extracted from one single known structure. Some representative results are shown in Figure 1, where structures obtained by SoPriM-PCA and those obtained by SoPriM-NMA for the H-Ras enzyme are compared via color-coded projections onto the top two variables utilized by each algorithm. The results show that precious information can be obtained on the energy landscape even when one structural model is available. The presented work opens up interesting venues of research on structure-based inference of dynamics. Acknowledgment: This work is supported in part by NSF Grant No. 1421001 to AS and NSF Grant No. 1440581 to AS and EP. Computations were run on ARGO, a research computing cluster provided by the Office of Research Computing at George Mason University, VA (URL: http://orc.gmu.edu).