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

Abstract

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.

Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods

“…It is important to obtain a set of suitable parameters in sscENM, which in this work are determined by maximizing the Pearson correlation coefficient (PCC) between the experimental and theoretical residue B -factors by systematically searching the parameters r c and c in the range of [7 Å, 13 Å]/[7 Å, 25 Å] and [1, 10]/[1, 10] with step size 1 Å and 1 for sscGNM/sscANM. , The PCC is defined as follows P C C = ∑ i = 1 N false( B i normalt normalh normale − B normalt normalh normale .25em ̅ false) · false( B i exp − B exp .25em ̅ false) ∑ i = 1 N false( B i normalt normalh normale − B normalt normalh normale .25em ̅ false) 2 · ∑ i = 1 N false( B i exp − B exp .25em ̅ false) 2 where B i the and B i exp are the theoretical and experimental B -factors of the i th node, respectively. The PCC value ranges from −1 to 1.…”

“…According to the Debye−Waller theory, the B-factors can be obtained through the following calculation [7 Å, 25 Å] and [1,10]/ [1,10] with step size 1 Å and 1 for sscGNM/sscANM. 13,26 The PCC is defined as follows…”

Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses

“…19 Recently in 2022, our group proposed a force constant fitted GNM based on MD ensembles (fcfGNM MD ) in which the force constants are directly computed from the inverse covariance matrix using a ridge-type operator for the precision matrix estimation (ROPE). 20 The method achieves an outstanding improvement in reproducing residue fluctuations and cross-correlations when compared with the traditional GNM. On the basis of this, here we construct the dynamic version of fcfGNM MD (dfcfGNM MD ) and calculate the transfer entropy similar to the method dGNM developed by Hacisuleyman 11 (the full computational details of the fcfGNM MD model and transfer entropy can be found in the Supporting Information).…”

“…Recently in 2022, our group proposed a force constant fitted GNM based on MD ensembles (fcfGNM MD ) in which the force constants are directly computed from the inverse covariance matrix using a ridge-type operator for the precision matrix estimation (ROPE) . The method achieves an outstanding improvement in reproducing residue fluctuations and cross-correlations when compared with the traditional GNM.…”

Celebrating Diversity, Equity, Inclusion, and Respect in Computational and Theoretical Chemistry

“…interrogate dissociation mechanisms of the transthyretin (TTR) tetramer into a monomer, through conventional and meta-dynamics simulations, providing guidance for understanding TTR-related amyloidosis and potential therapeutic avenues . Chen et al use Gaussian and amino acid-based network models to study allosteric signaling and communication mechanisms in opioid receptors . Zang et al employ a computational workflow combining targeted MD, the string method, and umbrella sampling to explore the conformational landscape of p38α and investigate the role of phosphorylation of Y323 in its autoactivation pathway .…”