Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach
Ikki Yasuda,
Katsuhiro Endo,
Eiji Yamamoto
et al.
Abstract: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 pr… Show more
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