A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry

Abstract

Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subject to environmental changes. Hydrogen deuterium exchange mass-spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions which hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment and improved interpretation in an epitope mapping experiment. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformation change on binding. Our findings are consistent with the co-crystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data.