Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Hydrogen
exchange (HX) studies have provided critical insight into our understanding
of protein folding, structure, and dynamics. More recently, hydrogen
exchange mass spectrometry (HX-MS) has become a widely applicable
tool for HX studies. The interpretation of the wealth of data generated
by HX-MS experiments as well as other HX methods would greatly benefit
from the availability of exchange predictions derived from structures
or models for comparison with experiment. Most reported computational
HX modeling studies have employed solvent-accessible-surface-area
based metrics in attempts to interpret HX data on the basis of structures
or models. In this study, a computational HX-MS prediction method
based on classification of the amide hydrogen bonding modes mimicking
the local unfolding model is demonstrated. Analysis of the NH bonding
configurations from molecular dynamics (MD) simulation snapshots is
used to determine partitioning over bonded and nonbonded NH states
and is directly mapped into a protection factor (PF) using a logistics
growth function. Predicted PFs are then used for calculating deuteration
values of peptides and compared with experimental data. Hydrogen exchange
MS data for fatty acid synthase thioesterase (FAS-TE) collected for
a range of pHs and temperatures was used for detailed evaluation of
the approach. High correlation between prediction and experiment for
observable fragment peptides is observed in the FAS-TE and additional
benchmarking systems that included various apo/holo proteins for which
literature data were available. In addition, it is shown that HX modeling
can improve experimental resolution through decomposition of in-exchange
curves into rate classes, which correlate with prediction from MD.
Successful rate class decompositions provide further evidence that
the presented approach captures the underlying physical processes
correctly at the single residue level. This assessment is further
strengthened in a comparison of residue resolved protection factor
predictions for staphylococcal nuclease with NMR data, which was also
used to compare prediction performance with other algorithms described
in the literature. The demonstrated transferable and scalable MD based
HX prediction approach adds significantly to the available tools for
HX-MS data interpretation based on available structures and models.