Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

Abstract

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

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Acknowledgements

The research leading to these results has been co-funded by the European Commission under the H2020 Research Infrastructures Contract No. 675121 (project VI-SEEM). Computational time was granted from the VI-SEEM project and the Greek National HPC facility—ARIS under the project ID “D3R”. Schrödinger representatives are acknowledged for their technical support. All data is available to download at: http://hdl.handle.net/21.15102/VISEEM-277

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Correspondence to Zoe Cournia.

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Athanasiou, C., Vasilakaki, S., Dellis, D. et al. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2. J Comput Aided Mol Des 32, 21–44 (2018). https://doi.org/10.1007/s10822-017-0075-9

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Keywords

  • Free energy perturbation
  • Docking
  • Farnesoid X receptor
  • FXR
  • D3R
  • FEP+
  • Drug design data resource