Improving the Reliability of Language Model-Predicted Structures as Docking Targets through Geometric Graph Learning

Applying artificial intelligence techniques to flexibly
model the
binding between the ligand and protein has attracted extensive interest
in recent years, but their applicability remains improved. In this
study, we have developed CarsiDock-Flex, a novel two-step flexible
docking paradigm that generates binding poses directly from predicted
structures. CarsiDock-Flex consists of an equivariant deep learning-based
model termed CarsiInduce to refine ESMFold-predicted protein pockets
with the induction of specific ligands and our existing CarsiDock
algorithm to redock the ligand into the induced binding pockets. Extensive
evaluations demonstrate the effectiveness of CarsiInduce, which can
successfully guide the transition of ESMFold-predicted pockets into
their holo-like conformations for numerous cases,
thus leading to the superior docking accuracy of CarsiDock-Flex even
on unseen sequences. Overall, our approach offers a novel design for
flexible modeling of protein–ligand binding poses, paving the
way for a deeper understanding of protein–ligand interactions
that account for protein flexibility.