gr Predictor: A Deep Learning Model for Predicting the Hydration Structures around Proteins

Among the factors affecting biological
processes such as protein
folding and ligand binding, hydration, which is represented by a three-dimensional
water site distribution function around the protein, is crucial. The
typical methods for computing the distribution functions, including
molecular dynamics simulations and the three-dimensional reference
interaction site model (3D-RISM) theory, require a long computation
time ranging from hours to tens of hours. Here, we propose a deep
learning (DL) model that rapidly estimates the distribution functions
around proteins obtained using the 3D-RISM theory from the protein
3D structure. The distribution functions predicted using our DL model
are in good agreement with those obtained using the 3D-RISM theory.
Particularly, the coefficient of determination between the distribution
function obtained by the DL model and that obtained using the 3D-RISM
theory is approximately 0.98. Furthermore, using a graphics processing
unit, the prediction by the DL model is completed in less than 1 min,
more than 2 orders of magnitude faster than the calculation time of
the 3D-RISM theory. The position of water molecules around the protein
was estimated based on the distribution function obtained by our DL
model, and the position of waters estimated by our DL model was in
good agreement with that of water molecules estimated using the 3D-RISM
theory and of crystallographic waters. Therefore, our DL model provides
a practical and efficient way to calculate the three-dimensional water
site distribution functions and to estimate the position of water
molecules around the protein. The program called “gr Predictor”
is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor.