  The latest biological findings observe that the traditional motionless
'lock-and-key' theory is not generally applicable because the receptor and
ligand are constantly moving. Nonetheless, remarkable changes in associated
atomic sites and binding pose can provide vital information in understanding
the process of drug binding. Based on this mechanism, molecular dynamics (MD)
simulations were invented as a useful tool for investigating the dynamic
properties of a molecular system. However, the computational expenditure limits
the growth and application of protein trajectory-related studies, thus
hindering the possibility of supervised learning. To tackle this obstacle, we
present a novel spatial-temporal pre-training method based on the modified
Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two
specially designed self-supervised learning tasks: an atom-level prompt-based
denoising generative task and a conformation-level snapshot ordering task to
seize the flexibility information inside MD trajectories with very fine
temporal resolutions. The ProtMD can grant the encoder network the capacity to
capture the time-dependent geometric mobility of conformations along MD
trajectories. Two downstream tasks are chosen, i.e., the binding affinity
prediction and the ligand efficacy prediction, to verify the effectiveness of
ProtMD through linear detection and task-specific fine-tuning. We observe a
huge improvement from current state-of-the-art methods, with a decrease of 4.3%
in RMSE for the binding affinity problem and an average increase of 13.8% in
AUROC and AUPRC for the ligand efficacy problem. The results demonstrate
valuable insight into a strong correlation between the magnitude of
conformation's motion in the 3D space (i.e., flexibility) and the strength with
which the ligand binds with its receptor.
