  Prediction of protein-ligand binding affinity is a major goal in drug
discovery. Generally, free energy gap is calculated between two states (e.g.,
ligand binding and unbinding). The energy gap implicitly includes the effects
of changes in protein dynamics induced by the binding ligand. However, the
relationship between protein dynamics and binding affinity remains unclear.
Here, we propose a novel method that represents protein behavioral change upon
ligand binding with a simple feature that can be used to predict protein-ligand
affinity. From unbiased molecular simulation data, an unsupervised deep
learning method measures the differences in protein dynamics at a
ligand-binding site depending on the bound ligands. A dimension-reduction
method extracts a dynamic feature that is strongly correlated to the binding
affinities. Moreover, the residues that play important roles in protein-ligand
interactions are specified based on their contribution to the differences.
These results indicate the potential for dynamics-based drug discovery.
