Abstract
Protein-ligand docking aids structure-based drug discovery by computationally modelling protein-ligand interactions. DINC (Docking INCrementally) is one approach to molecular docking that improved the docking of large ligands using a parallelized incremental meta-docking. Traditional docking tools, including DINC, explore the flexibility of the ligand in a single receptor binding pocket assuming limited flexibility of the receptor backbone. This simplifying assumption narrows down the docking search space but hinders successful docking for flexible receptors. DINC-Ensemble implicitly considers receptor backbone flexibility by running DINC docking in parallel on different receptor conformations. Inputs to DINC-Ensemble include (1) a ligand and (2) a list of different receptor conformations. For each ligand-receptor pair DINC-Ensemble performs incremental meta-docking in parallel. As a result, multiple ligand poses are generated in the binding pockets of different receptor conformations. These poses are then ranked, and the lowest scoring pose is selected. Two main outputs provided by a successful run of DINC-Ensemble are (1) the best scoring ligand poses and (2) a ranked list of selected receptor conformations. The best scoring ligand pose can be used to understand the interactions between the receptor and the ligand that influence the binding. The ranked list of receptor conformations shows the best receptor conformation fit for a given ligand and can provide insight into ligand-induced conformational selection. We provide DINC-Ensemble as a Python package and a free web server at https://dinc-ensemble.kavrakilab.rice.edu/.