A Residue-Resolved Bayesian Approach to Quantitative Interpretation of Hydrogen–Deuterium Exchange from Mass Spectrometry: Application to Characterizing Protein–Ligand Interactions

Characterization of interactions
between proteins and other molecules
is crucial for understanding the mechanisms of action of biological
systems and, thus, drug discovery. An increasingly useful approach
to mapping these interactions is measurement of hydrogen/deuterium
exchange (HDX) using mass spectrometry (HDX-MS), which measures the
time-resolved deuterium incorporation of peptides obtained by enzymatic
digestion of the protein. Comparison of exchange rates between apo-
and ligand-bound conditions results in a mapping of the differential
HDX (ΔHDX) of the ligand. Residue-level analysis of these data,
however, must account for experimental error, sparseness, and ambiguity
due to overlapping peptides. Here, we propose a Bayesian method consisting
of a forward model, noise model, prior probabilities, and a Monte
Carlo sampling scheme. This method exploits a residue-resolved exponential
rate model of HDX-MS data obtained from all peptides simultaneously,
and explicitly models experimental error. The result is the best possible
estimate of ΔHDX magnitude and significance for each residue
given the data. We demonstrate the method by revealing richer structural
interpretation of ΔHDX data on two nuclear receptors: vitamin
D-receptor (VDR) and retinoic acid receptor gamma (RORγ). The
method is implemented in HDX Workbench and as a standalone module
of the open source Integrative Modeling Platform.