Water Networks in Complexes between Proteins and FDA-Approved Drugs

Water molecules at protein–ligand interfaces are
often of
significant pharmaceutical interest, owing in part to the entropy
which can be released upon the displacement of an ordered water by
a therapeutic compound. Protein structures may not, however, completely
resolve all critical bound water molecules, or there may be no experimental
data available. As such, predicting the location of water molecules
in the absence of a crystal structure is important in the context
of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational
technique that is gaining popularity for the simulation of buried
water sites. In this work, we assess the ability of GCMC to accurately
predict water binding locations, using a dataset that we have curated,
containing 108 unique structures of complexes between proteins and
Food and Drug Administration (FDA)-approved small-molecule drugs.
We show that GCMC correctly predicts 81.4% of nonbulk crystallographic
water sites to within 1.4 Å. However, our analysis demonstrates
that the reported performance of water prediction methods is highly
sensitive to the way in which the performance is measured. We also
find that crystallographic water sites with more protein/ligand hydrogen
bonds and stronger electron density are more reliably predicted by
GCMC. An analysis of water networks revealed that more than half of
the structures contain at least one ligand-contacting water network.
In these cases, displacement of a water site by a ligand modification
might yield unexpected results if the larger network is destabilized.
Cooperative effects between waters should therefore be explicitly
considered in structure-based drug design.