Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins.

Abstract

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments provide unique insights into native protein structure and dynamics in solution but have limited spatial resolution. Molecular dynamics (MD) simulations, in contrast, allow for the exploration of protein conformational landscapes with an atomistic level of resolution. However, forcefield inaccuracies, crystallographic biases, and timescale limitations may compromise the validity of the final structural ensemble. Hydrogen Deuterium eXchange Ensemble Reweighting (HDXer) is a maximum entropy approach that was recently developed to (1) quantitatively predict hydrogen-deuterium exchange from a modeled conformational ensemble and (2) refine the candidate ensemble to best fit an experimental HDX-MS dataset. Here, we compare how various computational and experimental parameters impact the performance of HDXer, using extracellular regulated kinase 2 (ERK2) and hen egg white lysozyme (HEWL) as model proteins. Conventional molecular dynamics simulations of ERK2 and HEWL were performed in triplicate using the CHARMM36m, AMBER FF19SB, and Drude forcefields. After predicting the HDX rates of each candidate ensemble, HDXer was used to fit the simulated structures to the corresponding HDX-MS datasets. To assess the influence of simulation length, truncated and strided copies of the full-length simulations were generated. Other factors, including peptide charge state selection, ion mobility mass spectrometry, and independent HDX-MS data acquisitions, were also tested. We find that while some variables (e.g., high vs. low peptide charge states) do not have a significant effect, other parameters (e.g., early vs. late timepoint selection) directly correlate with the disagreement between calculated and experimental deuterium uptake. The results from this benchmarking inform the development of a recommended HDXer workflow and best practices for optimal data acquisition.