Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies
Filip Fratev
Filip Fratev*†‡ , Thomas Steinbrecher§, and Svava Ósk Jónsdóttir∥
† Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, 1101 N Campbell Street, El Paso, Texas 79902, United States
‡ Micar21 Ltd., Persenk 34B, 1407 Sofia, Bulgaria
§ Schrödinger GmbH, Dynamostrasse 13, 68165 Mannheim, Baden-Württemberg, Germany
∥ insilTox ApS, Sverigesvej 20B, DK-2800 Kongens Lyngby, Denmark
ACS Omega, 2018, 3 (4), pp 4357–4371
DOI: 10.1021/acsomega.8b00123
Publication Date (Web): April 20, 2018
Copyright © 2018 American Chemical Society
*E-mail: fratev@micar21.com.
Abstract
Estimating 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.