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CDOCKER and \(\lambda\)-dynamics for prospective prediction in D3R Grand Challenge 2

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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 \(\lambda\)-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 \(\AA\). 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 \(\AA\) 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 \(\lambda\)-dynamics techniques, including multisite \(\lambda\)-dynamics (MS\(\lambda\)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.

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Acknowledgements

We thank Michael Crowley for sharing a new implementation of replica-exchange in CHARMM upon which we based BP-REX with domdec.

Funding

This work is supported by Grants from the NIH (GM037554 and GM107233).

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Author notes

  1. Xinqiang Ding, Ryan L. Hayes and Jonah Z. Vilseck have contributed equally for this work.

Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, Univeristy of Michigan, Ann Arbor, MI, 48109, USA

    Xinqiang Ding, Murchtricia K. Charles & Charles L. Brooks III

  2. Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA

    Ryan L. Hayes, Jonah Z. Vilseck & Charles L. Brooks III

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  1. Xinqiang Ding
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  2. Ryan L. Hayes
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  5. Charles L. Brooks III
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Correspondence to Charles L. Brooks III.

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Ding, X., Hayes, R.L., Vilseck, J.Z. et al. CDOCKER and \(\lambda\)-dynamics for prospective prediction in D3R Grand Challenge 2. J Comput Aided Mol Des 32, 89–102 (2018). https://doi.org/10.1007/s10822-017-0050-5

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