Protein Structure Prediction from NMR Hydrogen–Deuterium Exchange Data

Amide
hydrogen–deuterium exchange (HDX) has long
been used to determine regional flexibility and binding
sites in proteins; however, the data are too sparse for full structural
characterization. Experiments that measure HDX rates, such as HDX-NMR,
have far higher throughput compared to structure determination via
X-ray crystallography, cryo-EM, or a full suite of NMR experiments.
Data from HDX-NMR experiments encode information on the protein structure,
making HDX a prime candidate to be supplemented by computational algorithms
for protein structure prediction. We have developed a methodology
to incorporate HDX-NMR data into ab initio protein
structure prediction using the Rosetta software framework to predict
structures based on experimental agreement. To demonstrate the efficacy
of our algorithm, we examined 38 proteins with HDX-NMR data available,
comparing the predicted model with and without the incorporation of
HDX data into scoring. The root-mean-square deviation (rmsd, a measure
of the average atomic distance between superimposed models) of the
predicted model improved by 1.42 Å on average after incorporating
the HDX-NMR data into scoring. The average rmsd improvement for the
proteins where the selected model rmsd changed after incorporating
HDX data was 3.63 Å, including one improvement of more than 11
Å and seven proteins improving by greater than 4 Å, with
12/15 proteins improving overall. Additionally, for independent verification,
two proteins that were not part of the original benchmark were scored
including HDX data, with a dramatic improvement of the selected model
rmsd of nearly 9 Å for one of the proteins. Moreover, we have
developed a confidence metric allowing us to successfully identify
near-native models in the absence of a native structure. Improvement
in model selection with a strong confidence measure demonstrates that
protein structure prediction with HDX-NMR is a powerful tool which
can be performed with minimal additional computational strain and
expense.