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We report the performance of our newly introduced Ensemble Docking withEnhanced sampling of pocket Shape (EDES) protocol coupled to a template-basedalgorithm to generate near-native ligand conformations in the 2019 iteration ofthe Grand Challenge organized by the D3R consortium. Using either AutoDock4.2or HADDOCK2.2 docking programs (each software in two variants of the protocol)our method generated native-like poses among the top 5 submitted for evaluationfor most of the 20 targets with similar performances. The protein selected forGC4 was the human beta-site amyloid precursor protein cleaving enzyme 1(BACE-1), a transmembrane aspartic-acid protease. We identified at least onepose whose heavy-atoms RMSD was less than 2.5 {\AA} from the nativeconformation for 16 (80%) and 17 (85%) of the twenty targets using AutoDock andHADDOCK, respectively. Dissecting the possible sources of errors revealed that:i) our EDES protocol (with minor modifications) was able to samplesub-{\aa}ngstrom conformations for all 20 protein targets, reproducing thecorrect conformation of the binding site within ~1 {\AA} RMSD; ii) as alreadyshown by some of us in GC3, even in the presence of near-native proteinstructures, a proper selection of ligand conformers is crucial for the successof ensemble-docking calculations. Importantly, our approach performed bestamong the protocols exploiting only structural information of the apo proteinto generate conformations of the receptor for ensemble-docking calculations.

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: Performance in pose prediction in the D3R-GC4