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

By Pradeep Anand Ravindranath, Stefano Forli, David S. Goodsell, Arthur J. Olson, Michel F. Sanner. 2015

580 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search (2019, background): “However, in the future, self-tunning stopping criteria may 12 AUTODOCK-GPU be developed to automatically identify convergence (e.g., AUTODOCKFR (39)), and hence, reduce the overall compu- tation time by preventing unnecessary evaluations from being performed past the point of convergence.”
SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina (2024, methodology): “For Vina, the PDB file is converted into a PDBQT file using the prepare_receptor command tool from the AutoDockFR (ADFR) Suite (version 1.0) ( 38 ).”
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, background): “Traditional Molecular Docking Approaches AutoDock and its variants [1, 17, 18, 19] are widely used in molecular docking.”
Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration (2017, background): “(3) The overall time (TO): All the clock time taken by the tool process run from start to finish, and equals the sum of the previous two times.”