Leveraging Hdx-Ms Solution Data and HDX Modeling to Refine Molecular Dynamics Ensembles and Gain an Atomistic Understanding of Biophysical Phenomena

Abstract

Probing the structural equilibrium that proteins and protein complexes adopt in solution is critical in understanding a multitude of its biophysical properties. Two common ways to probe these dynamic structures are hydrogen deuterium exchange coupled mass spectrometry (HDX-MS) and molecular dynamics simulations (MD). HDX-MS is a solution-based technique that reports on a system's structure and dynamics in solution at peptide level resolution. Although informative, the typical information obtained through HDX-MS studies remains largely qualitative and is limited by the experimentally attainable resolution. On the other hand, MD takes a starting high-resolution structure with a set of model parameters to simulate the system's motion over time. The resulting trajectory gives an atomistic view of the system. However, the time scales accessible to MD simulations are limited and can lead to under exploration of the conformational landscape. Enhanced computational sampling methods have been developed to more efficiently explore a system's conformational landscape. However, determining the biological relevance of these enhanced sampling methods remains difficult. Here we apply a workflow which leverages experimental HDX-MS data, through maximum entropy reweighing, to extract representative ensembles from enhanced MD simulations. These extracted ensembles yield an atomistic resolution model of a protein's/system's in-solution dynamics. We further demonstrate the functionality of this workflow in probing biophysical phenomena. Thus far we have shown the ability to use this workflow to extract representative ensembles of a proteins in-solution native state ensemble (PhuS), protein ligand binding (GAPDH), and identifying metastable protein folding intermediates (A1AT). These high-resolution models are then orthogonally verified through various other biophysical methods. The resulting ensembles are then used to generate further structure-function hypotheses or to begin computer aided drug design