Conformational Ensembles from Experimental Data and Computer Simulations

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Conformational Ensembles from Experimental Data
and Computer Simulations Poster Abstracts
99
64-POS Board 24
A Modern Approach to Determining and Displaying Conformational Ensembles
Ryan L. Melvin1,2, Ryan C. Godwin 1, Jiajie Xiao1 , William G. Thompson1, Kenneth S.
Berenhaut2, Freddie R. Salsbury Jr1.
2Wake 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.