HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen–Deuterium Exchange Data

Accurate modeling of protein–protein complex structures
is essential for understanding biological mechanisms. Hydrogen–deuterium
exchange (HDX) experiments provide valuable insights into binding
interfaces. Incorporating HDX data into protein complex modeling workflows
offers a promising approach to improve prediction accuracy. Here,
we developed HDXRank, a graph neural network (GNN)-based framework
for candidate structure ranking utilizing alignment with HDX experimental
data. Trained on a newly curated HDX data set, HDXRank captures nuanced
local structural features critical for accurate HDX profile prediction.
This versatile framework can be integrated with a variety of protein
complex modeling tools, transforming the HDX profile alignment into
a model quality metric. HDXRank demonstrates effectiveness at ranking
models generated by rigid docking or AlphaFold, successfully prioritizing
functionally relevant models and improving prediction quality across
all tested protein targets. These findings underscore HDXRank’s
potential to become a pivotal tool for understanding molecular recognition
in complex biological systems.