Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

64-POS Board 24 A Modern Approach to Determining and Displaying Conformational Ensembles Ryan L. Melvin 1,2 , Ryan C. Godwin 1 , Jiajie Xiao 1 , William G. Thompson 1 , Kenneth S. Berenhaut 2 , Freddie R. Salsbury Jr 1 . 2 Wake Forest University, Winston Salem, NC, USA. 1 Wake Forest University, Winston Salem, NC, USA, The ensemble nature of biopolymers makes arbitrary parameter choices when selecting micro and macro-states a significant source of bias and uncertainty. Most partitioning methods require users to either have some a priori knowledge about the system to be clustered or to tune parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of data sets containing multiple structural ensembles. After determining partitions, displaying ensembles in static print media remains a challenge. Using a single representative conformation of a biopolymer rather than an ensemble of states mistakenly conveys a static nature rather than the actual dynamic personality. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of ensembles of structural states with little loss of the visual simplicity of displaying a single representative conformation. This method includes a dynamic element in that it clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We also discuss how these methods apply to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures.

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