DSDPFlex: Flexible-Receptor Docking with GPU Acceleration

Molecular
docking is an essential tool in structure-based drug
discovery, widely utilized to model ligand–protein interactions
and enrich potential hits. Among the different docking strategies,
semiflexible docking (rigid-receptor and flexible-ligand model) is
the most popular, benefiting from its balance of docking accuracy
and speed. However, this approach ignores the conformational changes
of proteins and hence demands suitable protein conformations as input.
When the binding interaction adheres to an induced-fit model, flexible
methods such as molecular dynamics simulation can be utilized, but
they are computationally demanding. To balance between speed and accuracy,
the flexible docking approach is an effective choice, as exemplified
by AutoDock Vina and AutoDockFR, which treat selected protein side
chains as flexible parts. However, the efficiency of flexible docking
methods is yet to be improved for virtual screening usage. In this
article, we introduce DSDPFlex, an improved flexible-receptor docking
method accelerated by GPU parallelization. Beyond acceleration, optimizations
with respect to sampling, scoring, and search space are implemented
in DSDPFlex to further improve its capability in flexible tasks. In
cross-docking evaluation, DSDPFlex demonstrates superior accuracy
compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor
tasks. We also show the advantage of flexible-receptor methods on
suboptimal pockets and validate the advantage of DSDPFlex in screening
on apo and AlphaFold2-predicted structures. With
improvements in both efficiency and accuracy, DSDPFlex is expected
to hold potential in future docking-based studies.