Differences between G-Protein-Stabilized Agonist-GPCR Complexes and their Nanobody-Stabilized Equivalents

The full activation mechanism of the adenosine A1 receptor revealed by GaMD and Su-GaMD simulations

The full activation process of G protein-coupled receptor (GPCR) plays an important role in cellular signal transduction. However, it remains challenging to simulate the whole process in which the GPCR is recognized and activated by a ligand and then couples to the G protein on a reasonable simulation timescale. Here, we developed a molecular dynamics (MD) approach named supervised (Su) Gaussian accelerated MD (GaMD) by incorporating a tabu-like supervision algorithm into a standard GaMD simulation. By using this Su-GaMD method, from the active and inactive structure of adenosine A₁ receptor (A₁R), we successfully revealed the full activation mechanism of A₁R, including adenosine (Ado)-A₁R recognition, preactivation of A₁R, and A₁R-G protein recognition, in hundreds of nanoseconds of simulations. The binding of Ado to the extracellular side of A₁R initiates conformational changes and the preactivation of A₁R. In turn, the binding of G_i2 to the intracellular side of A₁R causes a decrease in the volume of the extracellular orthosteric site and stabilizes the binding of Ado to A₁R. Su-GaMD could be a useful tool to reconstruct or even predict ligand-protein and protein-protein recognition pathways on a short timescale. The intermediate states revealed in this study could provide more detailed complementary structural characterizations to facilitate the drug design of A₁R in the future.

Finding the Perfect Fit: Conformational Biosensors to Determine the Efficacy of GPCR Ligands

G protein-coupled receptors (GPCRs) are highly druggable targets that adopt numerous conformations. A ligand's ability to stabilize specific conformation(s) of its cognate receptor determines its efficacy or ability to produce a biological response. Identifying ligands that produce different receptor conformations and potentially discrete pharmacological effects (e.g., biased agonists, partial agonists, antagonists, allosteric modulators) is a major goal in drug discovery and necessary to develop drugs with better effectiveness and fewer side effects. Fortunately, direct measurements of ligand efficacy, via receptor conformational changes are possible with the recent development of conformational biosensors. In this review, we discuss classical efficacy models, including the two-state model, the ternary-complex model, and multistate models. We describe how nanobody-, transducer-, and receptor-based conformational biosensors detect and/or stabilize specific GPCR conformations to identify ligands with different levels of efficacy. In particular, conformational biosensors provide the potential to identify and/or characterize therapeutically desirable but often difficult to measure conformations of receptors faster and better than current methods. For drug discovery/development, several recent proof-of-principle studies have optimized conformational biosensors for high-throughput screening (HTS) platforms. However, their widespread use is limited by the fact that few sensors are reliably capable of detecting low-frequency conformations and technically demanding assay conditions. Nonetheless, conformational biosensors do help identify desirable ligands such as allosteric modulators, biased ligands, or partial agonists in a single assay, representing a distinct advantage over classical methods.

Mechanistic insights into dopaminergic and serotonergic neurotransmission - concerted interactions with helices 5 and 6 drive the functional outcome

Brain functions rely on neurotransmitters that mediate communication between billions of neurons. Disruption of this communication can result in a plethora of psychiatric and neurological disorders. In this work, we combine molecular dynamics simulations, live-cell biosensor and electrophysiological assays to investigate the action of the neurotransmitter dopamine at the dopaminergic D2 receptor (D2R). The study of dopamine and closely related chemical probes reveals how neurotransmitter binding translates into the activation of distinct subsets of D2R effectors (i.e.: Gi2, GoB, Gz and β-arrestin 2). Ligand interactions with key residues in TM5 (S5.42) and TM6 (H6.55) in the D2R binding pocket yield a dopamine-like coupling signature, whereas exclusive TM5 interaction is typically linked to preferential G protein coupling (in particular GoB) over β-arrestin. Further experiments for serotonin receptors indicate that the reported molecular mechanism is shared by other monoaminergic neurotransmitter receptors. Ultimately, our study highlights how sequence variation in position 6.55 is used by nature to fine-tune β-arrestin recruitment and in turn receptor signaling and internalization of neurotransmitter receptors.

Capturing Peptide-GPCR Interactions and Their Dynamics

Many biological functions of peptides are mediated through G protein-coupled receptors (GPCRs). Upon ligand binding, GPCRs undergo conformational changes that facilitate the binding and activation of multiple effectors. GPCRs regulate nearly all physiological processes and are a favorite pharmacological target. In particular, drugs are sought after that elicit the recruitment of selected effectors only (biased ligands). Understanding how ligands bind to GPCRs and which conformational changes they induce is a fundamental step toward the development of more efficient and specific drugs. Moreover, it is emerging that the dynamic of the ligand–receptor interaction contributes to the specificity of both ligand recognition and effector recruitment, an aspect that is missing in structural snapshots from crystallography. We describe here biochemical and biophysical techniques to address ligand–receptor interactions in their structural and dynamic aspects, which include mutagenesis, crosslinking, spectroscopic techniques, and mass-spectrometry profiling. With a main focus on peptide receptors, we present methods to unveil the ligand–receptor contact interface and methods that address conformational changes both in the ligand and the GPCR. The presented studies highlight a wide structural heterogeneity among peptide receptors, reveal distinct structural changes occurring during ligand binding and a surprisingly high dynamics of the ligand–GPCR complexes.

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

Mechanistic Insights into Specific G Protein Interactions with Adenosine Receptors

Coupling between G-protein-coupled receptors (GPCRs) and the G proteins is a key step in cellular signaling. Despite extensive experimental and computational studies, the mechanism of specific GPCR-G protein coupling remains poorly understood. This has greatly hindered effective drug design of GPCRs that are primary targets of ∼¹/₃ of currently marketed drugs. Here, we have employed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method to decipher the mechanism of the GPCR-G protein interactions. Adenosine receptors (ARs) were used as model systems based on very recently determined cryo-EM structures of the A₁AR and A_2AAR coupled with the G_i and G_s proteins, respectively. Changing the G_i protein to the G_s led to increased fluctuations in the A₁AR and agonist adenosine (ADO), while agonist 5'-N-ethylcarboxamidoadenosine (NECA) binding in the A_2AAR could be still stabilized upon changing the G_s protein to the G_i. Free energy calculations identified one stable low-energy conformation for each of the A₁AR-G_i and A_2AAR-G_s complexes as in the cryo-EM structures, similarly for the A_2AAR-G_i complex. In contrast, the ADO agonist and G_s protein sampled multiple conformations in the A₁AR-G_s system. GaMD simulations thus indicated that the A₁AR preferred to couple with the G_i protein to the G_s, while the A_2AAR could couple with both the G_s and G_i proteins, being highly consistent with experimental findings of the ARs. More importantly, detailed analysis of the atomic simulations showed that the specific AR-G protein coupling resulted from remarkably complementary residue interactions at the protein interface, involving mainly the receptor transmembrane 6 helix and the G_α α5 helix and α4-β6 loop. In summary, the GaMD simulations have provided unprecedented insights into the dynamic mechanism of specific GPCR-G protein interactions at an atomistic level.

The role of NMR spectroscopy in mapping the conformational landscape of GPCRs

Over recent years, nuclear magnetic resonance (NMR) spectroscopy has developed into a powerful mechanistic tool for the investigation of G protein-coupled receptors (GPCRs). NMR provides insights which underpin the dynamic nature of these important receptors and reveals experimental evidence for a complex conformational energy landscape that is explored during receptor activation resulting in signalling. NMR studies have highlighted both the dynamic properties of different receptor states as well as the exchange pathways and intermediates formed during activation, extending the static view of GPCRs obtained from other techniques. NMR studies can be undertaken in realistic membrane-like phospholipid environments and an ever-increasing choice of labelling strategies provides comprehensive, receptor-wide information. Combined with other structural methods, NMR is contributing to our understanding of allosteric signal propagation and the interaction of GPCRs with intracellular binding partners (IBP), crucial to explaining cellular signalling.

Recent advances in computational studies of GPCR-G protein interactions

Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches.

Binding, Thermodynamics, and Selectivity of a Non-peptide Antagonist to the Melanocortin-4 Receptor

The melanocortin-4 receptor (MC4R) is a potential drug target for treatment of obesity, anxiety, depression, and sexual dysfunction. Crystal structures for MC4R are not yet available, which has hindered successful structure-based drug design. Using microsecond-scale molecular-dynamics simulations, we have investigated selective binding of the non-peptide antagonist MCL0129 to a homology model of human MC4R (hMC4R). This approach revealed that, at the end of a multi-step binding process, MCL0129 spontaneously adopts a binding mode in which it blocks the agonistic-binding site. This binding mode was confirmed in subsequent metadynamics simulations, which gave an affinity for human hMC4R that matches the experimentally determined value. Extending our simulations of MCL0129 binding to hMC1R and hMC3R, we find that receptor subtype selectivity for hMC4R depends on few amino acids located in various structural elements of the receptor. These insights may support rational drug design targeting the melanocortin systems.

Multiple Binding Sites Contribute to the Mechanism of Mixed Agonistic and Positive Allosteric Modulators of the Cannabinoid CB1 Receptor

The cannabinoid CB1 receptor (CB1R) is an abundant metabotropic G-protein-coupled receptor that has been difficult to address therapeutically because of CNS side effects exerted by orthosteric drug candidates. Recent efforts have focused on developing allosteric modulators that target CB1R. Compounds from the recently discovered class of mixed agonistic and positive allosteric modulators (Ago-PAMs) based on 2-phenylindoles have shown promising functional and binding properties as CB1R ligands. Here, we identify binding modes of both the CP 55,940 agonist and GAT228, a 2-phenylindole allosteric modulator, by using our metadynamics simulation protocol, and quantify their affinity and cooperativity by atomistic simulations. We demonstrate the involvement of multiple adjunct binding sites in the Ago-PAM characteristics of the 2-phenylindole modulators and explain their ability to compete with orthosteric agonists at higher concentrations. We validate these results experimentally by showing the contribution of multiple sites on the allosteric binding of ZCZ011, another homologous member of the class, together with the orthosteric agonist.

Computational Exploration of Conformational Transitions in Protein Drug Targets

Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.