Ligand Docking Methods to Recognize Allosteric Inhibitors for G-Protein-Coupled Receptors
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ABSTRACT:
SUBMITTER: Harini K
PROVIDER: S-EPMC8558589 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.
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Project description:The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10(3) fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.
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Project description:G protein-coupled receptors (GPCRs) are allosteric proteins, because their signal transduction relies on interactions between topographically distinct, yet conformationally linked, domains. Much of the focus on GPCR allostery in the new millennium, however, has been on modes of targeting GPCR allosteric sites with chemical probes due to the potential for novel therapeutics. It is now apparent that some GPCRs possess more than one targetable allosteric site, in addition to a growing list of putative endogenous modulators. Advances in structural biology are also shedding new insights into mechanisms of allostery, although the complexities of candidate allosteric drugs necessitate rigorous biological characterization.
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Project description:The binding of ligands to G-protein-coupled receptors (GPCRs) in the extracellular region transmits the signal to the intracellular region to initiate coupling to effector proteins. The mechanism of this allosteric communication remains largely unexplored. Knowledge of the residues involved in the pipeline of the allosteric communication from the extracellular to the intracellular region will provide means to (a) design ligands with bias in potency towards one signaling pathway over others, and (b) design allosteric modulators that show subtype selectivity in GPCRs. In this review we describe the current state of the computational methods that provide insights into the allosteric communication in GPCRs and elucidate how this information can be used to design allosteric modulators.
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Project description:GPCRs (G-protein coupled receptors) are the largest family of drug targets and share a conserved structure. Binding sites are unknown for many important GPCR ligands due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our approach, ConDockSite, for predicting ligand binding sites in class A GPCRs using combined information from surface conservation and docking, starting from crystal structures or homology models. We demonstrate the effectiveness of ConDockSite on crystallized class A GPCRs such as the beta2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDockSite successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDockSite to predict the ligand binding sites on a structurally uncharacterized GPCR, GPER, the G-protein coupled estrogen receptor. Most of the sites predicted by ConDockSite match those found in other independent modeling studies. ConDockSite predicts that four ligands bind to a common location on GPER at a site deep in the receptor cleft. Incorporating sequence conservation information in ConDockSite overcomes errors introduced from physics-based scoring functions and homology modeling.
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Project description:The past few years have seen spectacular progress in the structure determination of G protein-coupled receptors (GPCRs). We now have structural representatives from classes A, B, C, and F. Within the rhodopsin-like class A, most structures belong to the α group, whereas fewer GPCR structures are available from the β, γ, and δ groups, which include peptide GPCRs such as the receptors for neurotensin (β group), opioids, chemokines (γ group), and protease-activated receptors (δ group). Structural information on peptide GPCRs is restricted to complexes with non-peptidic drug-like antagonists with the exception of the chemokine receptor CXCR4 that has been crystallized in the presence of a cyclic peptide antagonist. Notably, the neurotensin receptor 1 is to date the only peptide GPCR whose structure has been solved in the presence of a peptide agonist. Although limited in number, the current peptide GPCR structures reveal great diversity in shape and electrostatic properties of the ligand binding pockets, features that play key roles in the discrimination of ligands. Here, we review these aspects of peptide GPCRs in view of possible models for peptide agonist binding.
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Project description:Allosteric coupling describes a reciprocal process whereby G-protein-coupled receptors (GPCRs) relay ligand-induced conformational changes from the extracellular binding pocket to the intracellular signaling surface. Therefore, GPCR activation is sensitive to both the type of extracellular ligand and intracellular signaling protein. We hypothesized that ligand-specific allosteric coupling may result in preferential (i.e., biased) engagement of downstream effectors. However, the structural basis underlying ligand-dependent control of this essential allosteric mechanism is poorly understood. Here, we show that two sets of extended muscarinic acetylcholine receptor M1 agonists, which only differ in linker length, progressively constrain receptor signaling. We demonstrate that stepwise shortening of their chemical linker gradually hampers binding pocket closure, resulting in divergent coupling to distinct G-protein families. Our data provide an experimental strategy for the design of ligands with selective G-protein recognition and reveal a potentially general mechanism of ligand-specific allosteric coupling.
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Project description:Allosteric modulation of G protein-coupled receptors represent a promising mechanism of pharmacological intervention. Dramatic developments witnessed in the structural biology of membrane proteins continue to reveal that the binding sites of allosteric modulators are widely distributed, including along protein surfaces. Here we restrict consideration to intrahelical and intracellular sites together with allosteric conformational locks, and show that the protein mapping tools FTMap and FTSite identify 83% and 88% of such experimentally confirmed allosteric sites within the three strongest sites found. The methods were also able to find partially hidden allosteric sites that were not fully formed in X-ray structures crystallized in the absence of allosteric ligands. These results confirm that the intrahelical sites capable of binding druglike allosteric modulators are among the strongest ligand recognition sites in a large fraction of GPCRs and suggest that both FTMap and FTSite are useful tools for identifying allosteric sites and to aid in the design of such compounds in a range of GPCR targets.
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Project description:G protein-coupled Receptors (GPCRs) play a central role in many physiological processes and, consequently, constitute important drug targets. In particular, the search for allosteric drugs has recently drawn attention, since they could be more selective and lead to fewer side effects. Accordingly, computational tools have been used to estimate the druggability of allosteric sites in these receptors. In spite of many successful results, the problem is still challenging, particularly the prediction of hydrophobic sites in the interface between the protein and the membrane. In this work, we propose a complementary approach, based on dynamical correlations. Our basic hypothesis was that allosteric sites are strongly coupled to regions of the receptor that undergo important conformational changes upon activation. Therefore, using ensembles of experimental structures, normal mode analysis and molecular dynamics simulations we calculated correlations between internal fluctuations of different sites and a collective variable describing the activation state of the receptor. Then, we ranked the sites based on the strength of their coupling to the collective dynamics. In the β2 adrenergic (β2AR), glucagon (GCGR) and M2 muscarinic receptors, this procedure allowed us to correctly identify known allosteric sites, suggesting it has predictive value. Our results indicate that this dynamics-based approach can be a complementary tool to the existing toolbox to characterize allosteric sites in GPCRs.
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