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Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present  AutoDockFR – AutoDock  for Flexible Receptors ( ADFR ), a new docking engine based on the  AutoDock4  scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate  ADFR  using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in  AutoDock4 . We demonstrate greatly increased success rates when cross-docking ligands into  apo  receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 –a receptor diversity set containing 17 pairs of  apo-holo  structures; and 2) CDK2 –a ligand diversity set composed of one CDK2  apo  structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the  apo  conformation of the receptors with up to 14 flexible side-chains,  ADFR  reports more correctly cross-docked ligands than  AutoDock Vina  on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2.  ADFR  also outperforms  AutoDock Vina  in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the  holo  complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for  AutoDockFR  scales linearly when side-chain flexibility is added.

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility