Selection and Integration of Optimal Experiments for Refinement of Heterogeneous Conformational Ensembles

Multistructured biomolecular systems play crucial roles in a wide variety of cellular processes but have resisted traditional methods of structure determination which are often optimized to resolve only a few low-energy states. Experimental measurements that do yield data on multiple conformational populations remain extremely challenging, largely because multiple measurements cannot be performed simultaneously. This leads to two major limitations: the data are often sparse over atomic degrees of freedom, making experiment selection a critical step in conformational refinement, and difficult to integrate, particularly since separate measurements cannot provide information on the joint distribution. This work addresses these two outstanding challenges in refining heterogeneous conformational ensembles.

In Chapter 2, we develop a molecular simulations and information-theory based approach to select which double electron-electron resonance (DEER) experiments best refine conformational ensembles. The approach is tested on three flexible proteins. For proteins where a clear mechanistic hypothesis exists, experiments that test this hypothesis are systematically identified. When available data do not yield such mechanistic hypotheses, experiments that significantly outperform structure-guided approaches in conformational refinement are identified. This approach offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

In Chapter 3, we develop a method to incorporate sparse, multimultimodal spectroscopic data into high-resolution estimates of conformational ensembles. We have tested our method by integrating DEER measurements on the SNARE protein syntaxin-1a into biased molecular dynamics simulations. We find that our method substantially outperforms existing state-of-the-art methods in capturing syntaxin’s open–closed conformational equilibrium and further yields new conformational states that are consistent with experimental data and may help in understanding syntaxin’s function.

In Chapter 4, we develop a method to estimate conformational ensembles from multiple, separately-acquired measurements by inferring their joint distribution. We have tested the method on a simplified model of an alternating-access transporter and find that the method correctly estimates both the joint distribution and the conformational ensemble. Although the method is demonstrated on a toy system, it may be easily extended to more complex biological systems such as syntaxin.

Together, these three novel methods for refining heterogeneous conformational ensembles from spectroscopic data will greatly accelerate the structural understanding of such systems.