Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

76-POS Board 36 A Multi-crystal Parameterisation Method for Separating Atomic and Molecular Disorder

in Crystallographic Experiments Nicholas M. Pearce , Piet Gros. University of Utrecht, Utrecht, Utrecht, Netherlands.

Diffraction experiments result in a temporal and spatial average over many molecules in a crystal. Atomic Displacement Parameters (ADPs) model harmonic deviations from the average coordinates arising through thermal motion or crystal imperfections. The ADP for a particular atom therefore comprises contributions from multiple sources, including: crystal-dependent disorder; collective, molecule-dependent, “rigid body” motions; and any individual motions of the atom. Large-scale crystal-dependent factors can be considered an artifact of the crystallographic experiment, but collective and individual motions of atoms within a crystal may reveal subtle and biologically relevant protein motions. Translation-libration-screw (TLS) models are well-established as a method for describing collective motions of groups of atoms in a crystal. In general, however, the separation of the overall observed motion into the different contributions (crystal, rigid body, atomic) is ambiguous, since crystal-dependent factors cannot be uniquely separated from crystal- independent factors. Furthermore, overfitting is ever-present, and the complexity of a ADP model is dictated by the resolution of the crystallographic data. To overcome the intrinsic obstacles of parameterising disorder in a single crystallographic dataset, we present a multi-dataset ADP-parameterisation approach for modelling atomic disorder: by characterising the ADPs across a series of datasets simultaneously, using a series of TLS models, we separate crystal-dependent and crystal-independent parameters. This results in a hierarchical model of motion, allowing e.g. atomic motions of a sidechain to be decoupled from the large-scale motions of the whole molecule. This approach is validated by both a reduction in the R-free/R-work gap across the set of datasets and a decrease in R-free: the multi-dataset parameterisation thus not only limits overfitting, but increases overall model quality.

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