FlexVDW: A machine learning approach to account for protein flexibility in ligand docking
This addresses the challenge of ligand docking accuracy for drug discovery when proteins deform upon binding, though it is an incremental improvement over existing methods.
The paper tackled the problem of inaccurate ligand docking due to rigid protein assumptions by developing a deep learning model to implicitly account for protein flexibility in van der Waals energy predictions, resulting in improved pose prediction in cases with substantial protein deformation without degrading performance in minimal deformation cases.
Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to take receptor flexibility into account implicitly when predicting van der Waals energy. We show that incorporating this machine-learned energy term into a state-of-the-art physics-based scoring function improves small molecule ligand pose prediction results in cases with substantial protein deformation, without degrading performance in cases with minimal protein deformation. This work demonstrates the feasibility of learning effects of protein flexibility on ligand binding without explicitly modeling changes in protein structure.