Abstract Hydrogen–deuterium exchange mass spectrometry (HDX-MS) is an established technique that measures the exchange rate of amide hydrogens, with this exchange rate being a surrogate for protein conformational dynamics. Advances in instrumentation, automation, and data analysis have transformed HDX-MS into a high-throughput and highly reproducible method capable of probing complex biological systems and addressing key questions that have been challenging to study by other structural biology approaches. By enabling measurement of amide exchange rates and mapping of differential exchange between distinct conformational states, HDX-MS provides insight into both allosteric transitions and protein interaction interfaces. Recent advances in the capabilities of artificial intelligence (AI) have been rapidly adopted by structural biology, leading to an unprecedented expansion in the quantity and accessibility of structural predictions, underscoring the need for experimental methods to validate these predicted models and provide insight into both epitopes and allosteric conformational changes. This is particularly critical for non-evolutionarily driven interactions such as antibodies, nanobodies, and artificially designed proteins, where AI technologies can yield false positives. This review highlights how HDX-MS can be integrated synergistically into modern structural biology workflows (cryo-EM, X-ray crystallography, and AI-enabled modeling) and how combining these approaches can be powerful to advance our mechanistic understanding of complex biological processes.
Nyvall et al. (2026) studied this question.