Insights into unbound–bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations

<p>G protein coupled receptors (GPCRs) are source machinery in signal transduction pathways and being one of the major therapeutic targets play a significant in drug discovery. GPR142, an orphan GPCR, has been implicated in the regulation of insulin, thereby having a crucial role in Type II diabetes management. Deciphering of the structures of orphan, GPCRs (O-GPCRs) offer better prospects for advancements in research in ion translocation and transduction of extracellular signals. As the crystallographic structure of GPR142 is not available in PDB, therefore, threading and <i>ab initio</i>-based approaches were used for 3D modeling of GPR142. Molecular dynamic simulations (900 ns) were performed on the 3D model of GPR142 and complexes of GPR142 with top five hits, obtained through virtual screening, embedded in lipid bilayer with aqueous system using OPLS force field. Compound 1, 3, and 4 may act as scaffolds for designing potential lead agonists for GPR142. The finding of GPR142 MD simulation study provides more comprehensive representation of the functional properties. The concern for Type II diabetes is increasing worldwide and successful treatment of this disease demands novel drugs with better efficacy.</p

Predicted antigenic B-cell epitopes of hrHPVs E6 proteins.

<p>A total of 44 antigenic sites were identified from all the E6 proteins. Residues shared by both B-cell and T-cell epitopes are given in bold.</p

Molecular interaction analysis of predicted HPVs E6 peptides docked to MHC-I HLA-A*0101.

<p>(A) ETEVLDFAF, (B) RSEVYDFAF, (C) FQDPAERPY, (D) QTEVYEFAF, (E) ATLERTEVY, (F) EATIKKSLY, (G) FTDLRIVYR, (H) LCDLLIRCY, (I) KTLQRSEVY, (J) ETITNTKLY.</p

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches - Fig 5

<p><b>(A)</b> Pharmacokinetics simulation showing the pathway of degrading the p53 genes in HPV infections, while in <b>(B)</b> X-axis represents the transition time of entities and Y-axis represent the concentration of the peptides.</p

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

<div><p>High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.</p></div

Molecular interaction analysis of predicted HPVs E6 peptides docked to MHC-I HLA-C*0801.

<p>Epitopes (A) FQDPAERPY, (B) ATLERTEVY and (C) FTDLRIVYR shown in the figure above was docked against HLA-C*0801.</p

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches - Fig 6

<p><b>(A)</b> The binding affinities (Global energies, vdW, electrostatic interactions) of all the docked epitopes are given in the graph. <b>(B)</b> Plot of RMSD of the backbone structures of MHCI-peptides complexes after 20ns simulation. The RMSD graph of each complex is shown in different colors. The graph (along x-axis is number of residues, y-axis RMSD in nm) is showing the stable movement of all the complexes.</p

HPV E6 peptides–HLA-A*0101 interaction.

<p>FireDock energy for the best ranked complex initial distance between the H-bond donor and the acceptor; measured with the Find H.Bond tool in Chimera (H-Bond constraints were relaxed by 1 Å and 20.0 degrees) distance between the H-bond donor and the acceptor after molecular dynamics simulation (MD); measured in PyMOL, nd = no detected H-bond.</p

HPV E6 peptides–HLA-B*1501 and HLA-B*5801 interactions.

<p>FireDock energy for the best ranked complex initial distance between the H-bond donor and the acceptor; measured with the Find H.Bond tool in Chimera (H-Bond constraints were relaxed by 1 Å and 20.0 degrees) distance between the H-bond donor and the acceptor after molecular dynamics simulation (MD); measured in PyMOL, nd = no detected H-bond.</p

Molecular interaction analysis of predicted HPVs E6 peptides docked to MHC-I HLA-B*1501 and HLA-B*5801.

<p>Epitope A and B (ATLERTEVY, KTLQRSEVY) were docked against HLA-B*1501 while epitope C (KTLQRSEVY) shown in the figure above was docked against HLA-B*5801.</p