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J Comput Aided Mol Des. 2018 Jan;32(1):89-102. doi: 10.1007/s10822-017-0050-5. Epub 2017 Sep 7.

CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.

Author information

1
Department of Computational Medicine and Bioinformatics, Univeristy of Michigan, Ann Arbor, MI, 48109, USA.
2
Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA.
3
Department of Computational Medicine and Bioinformatics, Univeristy of Michigan, Ann Arbor, MI, 48109, USA. brookscl@umich.edu.
4
Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA. brookscl@umich.edu.

Abstract

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and [Formula: see text]-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 [Formula: see text]. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 [Formula: see text] for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For [Formula: see text]-dynamics techniques, including multisite [Formula: see text]-dynamics (MS[Formula: see text]D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

KEYWORDS:

CDOCKER; D3R; Drug design data resources; Free energy calculation; Protein-ligand docking; dynamics

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