HDX-MS, Molecular Dynamics, and Modeling: An Integrative Approach to Model Solution Structural Ensembles

Abstract

Probing the structural equilibrium that proteins and protein complexes can adopt in solution is critical in understanding a multitude their 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 secondary and tertiary structure and dynamics at peptide level resolution. Although informative, the typical information obtained through HDX-MS studies remains largely qualitative and is limited by the attainable resolution. On the other hand, MD takes a static 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 from which a multitude of biophysical properties can be derived. However, the timescales accessible to MD simulations are limited and can lead to an under exploration of the conformational landscape. To overcome this, enhanced computational sampling methods have been developed to more efficiently explore a system’s conformational landscape. The ability to integrate experimental HDX-MS data with MD simulations has the potential to increase the utility of both methods. Such integration rests on the ability to predict deuterium exchange from computationally generated ensembles. Thus far, several physics-based models of HDX exchange have been developed and implemented in the calculation of HDX exchange rates from MD simulations. Whereas the value of any single model remains a subject of debate, studies have not focused on the application of such integration to address unanswered biophysical questions. In this project, I aim to demonstrate the applicability of such integrative approach to a variety of biophysical questions. HDX exchange rates will be calculated from MD simulations and compared to the experimentally observed exchange rates for given systems. Further, utilizing a maximum entropy reweighting method, structural ensembles most consistent with in solution HDX-MS data will be extracted for analysis. In this thesis, I apply enhanced sampling MD, experimental HDX rates, and maximum entropy reweighting to generate realistic structural ensembles of protein and protein complexes in-solution to be used in the characterization of in-solution native state ensembles, protein conformational transitions, and protein-small molecule interactions. This is done using three model systems: the Cytoplasmic Heme Binding Protein (PhuS) from Pseudomonas aeruginosa, Human Plasminogen activator inhibitor-1, and ERK2 and its known type I inhibitors. This thesis develops, optimizes, and validates a workflow which can help shift HDX-MS studies from its current qualitative perspective to a quantitative treatment of HDX-MS which leverages computational simulations and extract atomic resolution interpretations.