Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design

Molecular interactions between receptor molecules (Chain A) and vaccine construct (Chain B).

Molecular interactions between receptor molecules (Chain A) and vaccine construct (Chain B).</p

Fig 7 -

Molecular dynamics (MD) simulation results A) C7-putative nitroreductase RMSD analysis B) RMSF analysis of Cα atoms C) H-bond estimation during 100 ns simulation D) Radius of gyration (Rg) analysis.</p

Fig 6 -

(A) The active site of the protein CD630_32050 was used to create a pharmacophore model. The characteristics are denoted by different colors. White represents a hydrogen-bond donor, yellow represents a hydrogen acceptor, green represents hydrophobic properties, and aromatic represents aromatic features (pink). (B) The molecular interactions of the top hit docked compound (CD630_32050) within the substrate-binding site. The nature of protein-ligand interactions is shown in different colors.</p

Population coverage of MHC-I and MHC-II alleles of the selected epitopes.

Population coverage of MHC-I and MHC-II alleles of the selected epitopes.</p

Druggability analysis of shortlisted non-human homologous pathogenic essential proteins.

Druggability analysis of shortlisted non-human homologous pathogenic essential proteins.</p

Secondary structure prediction of vaccine construct.

Secondary structure prediction of vaccine construct.</p

Vaccine candidate proteins finalized based on antigenicity, allergenicity, and toxicity parameters.

Vaccine candidate proteins finalized based on antigenicity, allergenicity, and toxicity parameters.</p

Pharmit scores and RMSD values of the top 10 hit compounds obtained from pharmacophore-based virtual screening using the Pharmit server.

Pharmit scores and RMSD values of the top 10 hit compounds obtained from pharmacophore-based virtual screening using the Pharmit server.</p