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 <i>holo</i>-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.