Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

Ligand
docking is a widely used tool for lead discovery and binding
mode prediction based drug discovery. The greatest challenges in docking
occur when the receptor significantly reorganizes upon small molecule
binding, thereby requiring an induced fit docking (IFD) approach in
which the receptor is allowed to move in order to bind to the ligand
optimally. IFD methods have had some success but suffer from a lack
of reliability. Complementing IFD with all-atom molecular dynamics
(MD) is a straightforward solution in principle but not in practice
due to the severe time scale limitations of MD. Here we introduce
a metadynamics plus IFD strategy for accurate and reliable prediction
of the structures of protein–ligand complexes at a practically
useful computational cost. Our strategy allows treating this problem
in full atomistic detail and in a computationally efficient manner
and enhances the predictive power of IFD methods. We significantly
increase the accuracy of the underlying IFD protocol across a large
data set comprising 42 different ligand–receptor systems. We
expect this approach to be of significant value in computationally
driven drug design.