PPARγ non-covalent antagonists exhibit mutable binding modes with a similar free energy of binding: a case study

<p>The structural and dynamical properties of PPARγ receptor in a complex with either partial or full agonists have been intensively studied but little is known about the receptor antagonistic conformation. A composition of microsecond accelerated molecular dynamics (aMD) simulation show that like partial agonists a non-covalent PPARγ full antagonist can bind in different modes of similar population size and free energies of binding. Four different and periodically exchanging ligand conformations are detected and described. The studied antagonist interacts with different receptor substructures and affects both the co-activator and the Cdk5 phosphorylation sites and, presumably, the natural complex with the DNA. However, no significant changes in the conformational states of the activation helix 12, and in particular an antagonist orientation, have been recorded. Finally, our results show also that the aMD approach can be successfully used in recovering the possible binding modes, considering fully the receptor flexibility, and is not dependent on the starting conformation.</p

Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

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

Combination of Genetic Screening and Molecular Dynamics as a Useful Tool for Identification of Disease-Related Mutations: ZASP PDZ Domain G54S Mutation Case

Cypher/ZASP (<i>LDB3</i> gene) is known to interact with a network of proteins. It binds to α-actinin and the calcium voltage channels (LTCC) via its PDZ domain. Here we report the identification of a highly conserved ZASP G54S mutation classified as a variant of unknown significance in a sample of an adult with hypertrophic cardiomyopathy (HCM). The initial bioinformatics calculations strongly evaluated G54S as damaging. Furthermore, we employed accelerated and classical molecular dynamics and free energy calculations to study the structural impact of this mutation on the ZASP apo form and to address the question of whether it can be linked to HCM. Seventeen independent MD runs and simulations of 2.5 μs total were performed and showed that G54S perturbs the α2 helix position via destabilization of the adjacent loop linked to the β5 sheet. This also leads to the formation of a strong H-bond between peptide target residues Leu17 and Gln66, thus restricting both the α-actinin2 and LTCC C-terminal peptides to access their natural binding site and reducing in this way their binding capacity. On the basis of these observations and the adult’s clinical data, we propose that ZASP^G54S and presumably other ZASP PDZ domain mutations can cause HCM. To the best of our knowledge, this is the first reported ZASP PDZ domain mutation that might be linked to HCM. The integrated workflow used in this study can be applied for the identification and description of other mutations that might be related to particular diseases

Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

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

Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

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

Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies

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

Discovery of a Novel Selective PPARγ Ligand with Partial Agonist Binding Properties by Integrated <i>in Silico</i>/<i>in Vitro</i> Work Flow

Full agonists to the peroxisome proliferator-activated receptor (PPAR)­γ, such as Rosiglitazone, have been associated with a series of undesired side effects, such as weight gain, fluid retention, cardiac hypertrophy, and hepatotoxicity. Nevertheless, PPARγ is involved in the expression of genes that control glucose and lipid metabolism and is an important target for drugs against type 2 diabetes, dyslipidemia, atherosclerosis, and cardiovascular disease. In an effort to identify novel PPARγ ligands with an improved pharmacological profile, emphasis has shifted to selective ligands with partial agonist binding properties. Toward this end we applied an integrated <i>in silico</i>/<i>in vitro</i> workflow, based on pharmacophore- and structure-based virtual screening of the ZINC library, coupled with competitive binding and transactivation assays, and adipocyte differentiation and gene expression studies. Hit compound <b>9</b> was identified as the most potent ligand (IC₅₀ = 0.3 μM) and a relatively poor inducer of adipocyte differentiation. The binding mode of compound <b>9</b> was confirmed by molecular dynamics simulation, and the calculated free energy of binding was −8.4 kcal/mol. A novel functional group, the carbonitrile group, was identified to be a key substituent in the ligand–protein interactions. Further studies on the transcriptional regulation properties of compound <b>9</b> revealed a gene regulatory profile that was to a large extent unique, however functionally closer to that of a partial agonist