ao8b00123_si_002.pdb (175.87 kB)
Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies
dataset
posted on 2018-04-20, 12:19 authored by Filip Fratev, Thomas Steinbrecher, Svava Ósk JónsdóttirEstimating the correct
binding modes of ligands in protein–ligand
complexes is crucial not only in the drug discovery process but also
for elucidating potential toxicity mechanisms. In the current paper,
we propose a computational modeling workflow using the combination
of docking, classical molecular dynamics (cMD), accelerated molecular
dynamics (aMD) and free-energy perturbation (FEP+ protocol) for identification
of possible ligand binding modes. It was applied for investigation
of selected perfluorocarboxyl acids (PFCAs) in the PPARγ nuclear
receptor. Although both regular and induced fit docking failed to
reproduce the experimentally determined binding mode of the ligands
when docked into a non-native X-ray structure, cMD and aMD simulations
successfully identified the most probable binding conformations. Moreover,
multiple binding modes were identified for all of these compounds
and the shorter-chain PFCAs continuously moved between a few energetically
favorable binding conformations. On the basis of MD predictions of
binding conformations, we applied the default and also redesigned
FEP+ sampling protocols, which accurately reproduced experimental
differences in the binding energies. Thus, the preliminary MD simulations
can also provide helpful information about correct setup of the FEP+
calculations. These results show that the PFCA binding modes were
accurately predicted and that the FEP+ protocol can be used to estimate
free energies of binding of flexible ligands that are not typical
druglike compounds. Our in silico workflow revealed the specific ligand–residue
interactions within the ligand binding domain and the main characteristics
of the PFCAs, and it was concluded that these compounds are week PPARγ
partial agonists. This work also suggests a common pipeline for identification
of ligand binding modes, ligand–protein dynamics description,
and relative free-energy calculations.