Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data

In
recent years mass spectrometry-based covalent labeling techniques
such as hydroxyl radical footprinting (HRF) have emerged as valuable
structural biology techniques, yielding information on protein tertiary
structure. These data, however, are not sufficient to predict protein
structure unambiguously, as they provide information only on the relative
solvent exposure of certain residues. Despite some recent advances,
no software currently exists that can utilize covalent labeling mass
spectrometry data to predict protein tertiary structure. We have developed
the first such tool, which incorporates mass spectrometry derived
protection factors from HRF labeling as a new centroid score term
for the Rosetta scoring function to improve the prediction of protein
tertiary structures. We tested our method on a set of four soluble
benchmark proteins with known crystal structures and either published
HRF experimental results or internally acquired data. Using the HRF
labeling data, we rescored large decoy sets of structures predicted
with Rosetta for each of the four benchmark proteins. As a result,
the model quality improved for all benchmark proteins as compared
to when scored with Rosetta alone. For two of the four proteins we
were even able to identify atomic resolution models with the addition
of HRF data.