Understanding Ligand Binding Selectivity in a Prototypical GPCR Family

A universal method for surface-based binding assays by preparing immobilized β2-adrenergic receptor stationary phase using solid binding peptide as a fusion tag

Protein functionalized surface has the potential to develop new assays for determining the drug-like properties of potential compounds and discovering specific partners of G protein-coupled receptors (GPCRs). However, a universal method for purifying and immobilizing functional GPCRs has remained elusive. To this end, we developed a general and rapid way to purify and immobilize β2-adrenergic receptor (β2AR) by silicon-specific peptide. We screened CotB1p as a tag from six silica-binding peptides (minTBP-1, CotB1p, SB7, Car9, and Si4-1) by examining their affinity to macroporous silica gel. We investigated the adsorption and desorption of CotB1p-tagged β2-adrenoceptor (β2AR-CotB1p) under diverse conditions to propose a protocol for receptor purification and immobilization. Under optimized conditions, β2AR immobilization were achieved by directly immersing cell lysates harboring the receptor with silica gel, and the elution of the receptor without demonstratable contaminants was realized by including l-arginine/L-lysine in the elutes. This allows purification of the receptor from Escherichia coli (E.coli) lysates with a purity of 95 %. The immobilized receptor was utilized as a stationary phase to reveal the tag impact on ligand-binding outputs by comparing the CotB1p-strategy with a typical covalent method. The KAs of salbutamol, chlorprenaline, tulobuterol, and terbutaline on β2AR-CotB1p column were 1.26 × 106, 6.59 × 106, 7.90 × 106, and 8.97 × 105 M-1 respectively, which were two orders of magnitude higher than those on the Halo-β2AR column. The whole immobilization was accomplished within 30 min without the requirement of any special treatment, resulting in enhanced accuracy for determining receptor-ligand binding parameters. Taken together, CotB1p-mediated strategy is simple, rapid, and universal for purification or immobilization of unstable biomolecules like GPCRs for analytical and biological applications.

Ligand-Directed Labeling of the Adenosine A1 Receptor in Living Cells

The study of protein function and dynamics in their native cellular environment is essential for progressing fundamental science. To overcome the requirement of genetic modification of the protein or the limitations of dissociable fluorescent ligands, ligand-directed (LD) chemistry has most recently emerged as a complementary, bioorthogonal approach for labeling native proteins. Here, we describe the rational design, development, and application of the first ligand-directed chemistry approach for labeling the A1AR in living cells. We pharmacologically demonstrate covalent labeling of A1AR expressed in living cells while the orthosteric binding site remains available. The probes were imaged using confocal microscopy and fluorescence correlation spectroscopy to study A1AR localization and dynamics in living cells. Additionally, the probes allowed visualization of the specific localization of A1ARs endogenously expressed in dorsal root ganglion (DRG) neurons. LD probes developed here hold promise for illuminating ligand-binding, receptor signaling, and trafficking of the A1AR in more physiologically relevant environments.

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

Learning Protein-Ligand Unbinding Pathways via Single-Parameter Community Detection

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the comprehension of paths such systems take between metastable states. In MD simulations, paths are usually not observable per se, but they need to be inferred from simulation trajectories. Here, we present a novel approach to cluster trajectories based on a community detection algorithm that necessitates only the definition of a single parameter. The unbinding of the streptavidin-biotin complex is used as a benchmark system and the A2a adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application. We demonstrate how such clusters of trajectories correspond to pathways and how the approach helps in the identification of reaction coordinates for a considered (un)binding process.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors

A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.

Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors

Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.

Stereochemistry-Driven Interactions of α,γ-Peptide Ligands with the Neuropeptide Y Y4-Receptor

The G-protein-coupled Y₄-receptor (Y₄R) and its endogenous ligand, pancreatic polypeptide (PP), suppress appetite in response to food intake and, thus, are attractive drug targets for body-weight control. The C-terminus of human PP (hPP), T³²-R³³-P³⁴-R³⁵-Y³⁶-NH₂, penetrates deep into the binding pocket with its tyrosine-amide and di-arginine motif. Here, we present two C-terminally amidated α,γ-hexapeptides (1a/b) with sequence Ac-R³¹-γ-CBAA³²-R³³-L³⁴-R³⁵-Y³⁶-NH₂, where γ-CBAA is the (1R,2S,3R)-configured 2-(aminomethyl)-3-phenylcyclobutanecarboxyl moiety (1a) or its mirror image (1b). Both peptides bind the Y₄R (K_i of 1a/b: 0.66/12 nM) and act as partial agonists (intrinsic activity of 1a/b: 50/39%). Their induced-fit binding poses in the Y₄R pocket are unique and build ligand-receptor contacts distinct from those of the C-terminus of the endogenous ligand hPP. We conclude that energetically favorable interactions, although they do not match those of the native ligand hPP, still guarantee high binding affinity (with 1a rivaling hPP) but not the maximum receptor activation.

Selective Deuteration Improves the Affinity of Adenosine A2A Receptor Ligands: A Computational Case Study with Istradefylline and Caffeine

We used a range of computational techniques to assess the effect of selective C-H deuteration on the antagonist istradefylline affinity for the adenosine A_2A receptor, which was discussed relative to its structural analogue caffeine, a well-known and likely the most widely used stimulant. The obtained results revealed that smaller caffeine shows high receptor flexibility and exchanges between two distinct poses, which agrees with crystallographic data. In contrast, the additional C8-trans-styryl fragment in istradefylline locks the ligand within a uniform binding pose, while contributing to the affinity through the C-H···π and π···π contacts with surface residues, which, together with its much lower hydration prior to binding, enhances the affinity over caffeine. In addition, the aromatic C8-unit shows a higher deuteration sensitivity over the xanthine part, so when both of its methoxy groups are d₆-deuterated, the affinity improvement is -0.4 kcal mol^-1, which surpasses the overall affinity gain of -0.3 kcal mol^-1 in the perdeuterated d₉-caffeine. Yet, the latter predicts around 1.7-fold potency increase, being relevant for its pharmaceutical implementations, and also those within the coffee and energy drink production industries. Still, the full potential of our strategy is achieved in polydeuterated d₁₉-istradefylline, whose A_2A affinity improves by -0.6 kcal mol^-1, signifying a 2.8-fold potency increase that strongly promotes it as a potential synthetic target. This knowledge supports deuterium application in drug design, and while the literature already reports about over 20 deuterated drugs currently in the clinical development, it is easily foreseen that more examples will hit the market in the years to come. With this in mind, we propose that the devised computational methodology, involving the ONIOM division of the QM region for the ligand and the MM region for its environment, with an implicit quantization of nuclear motions relevant for the H/D exchange, allows fast and efficient estimates of the binding isotope effects in any biological system.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A₁ and A_2A adenosine receptors (A₁R and A_2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A₁R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A₁R selective ligands were predicted. Of these, seven antagonized the A₁R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A₁R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Biased Activation Mechanism Induced by GPCR Heterodimerization: Observations from μOR/δOR Dimers

GPCRs regulate multiple intracellular signaling cascades. Biasedly activating one signaling pathway over the others provides additional clinical utility to optimize GPCR-based therapies. GPCR heterodimers possess different functions from their monomeric states, including their selectivity to different transducers. However, the biased signaling mechanism induced by the heterodimerization remains unclear. Motivated by the issue, we select an important GPCR heterodimer (μOR/δOR heterodimer) as a case and use microsecond Gaussian accelerated molecular dynamics simulation coupled with potential of mean force and protein structure network (PSN) to probe mechanisms regarding the heterodimerization-induced constitutive β-arrestin activity and efficacy change of the agonist DAMGO. The results show that only the lowest energy state of the μOR/δOR heterodimer, which adopts a slightly outward shift of TM6 and an ICL2 conformation close to the receptor core, can selectively accommodate β-arrestins. PSN further reveals important roles of H8, ICL1, and ICL2 in regulating the constitutive β-arrestin-biased activity for the apo μOR/δOR heterodimer. In addition, the heterodimerization can allosterically alter the binding mode of DAMGO mainly by means of W7.35. Consequently, DAMGO transmits the structural signal mainly through TM6 and TM7 in the dimer, rather than TM3 similar to the μOR monomer, thus changing the efficacy of DAMGO from a balanced agonist to the β-arrestin-biased one. On the other side, the binding of DAMGO to the heterodimer can stabilize μOR/δOR heterodimers through a stronger interaction of TM1/TM1 and H8/H8, accordingly enhancing the interaction of μOR with δOR and the binding affinity of the dimer to the β-arrestin. The agonist DAMGO does not change main compositions of the regulation network from the dimer interface to the transducer binding pocket of the μOR protomer, but induces an increase in the structural communication of the network, which should contribute to the enhanced β-arrestin coupling. Our observations, for the first time, reveal the molecular mechanism of the biased signaling induced by the heterodimerization for GPCRs, which should be beneficial to more comprehensively understand the GPCR bias signaling.

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.

Exploring Aspartic Protease Inhibitor Binding to Design-Selective Antimalarials

Selectivity is a major issue in the development of drugs targeting pathogen aspartic proteases. Here, we explore the selectivity-determining factors by studying specifically designed malaria aspartic protease (plasmepsin) open-flap inhibitors. Metadynamics simulations are used to uncover the complex binding/unbinding pathways of these inhibitors and describe the critical transition states in atomistic resolution. The simulation results are compared with experimentally determined enzymatic activities. Our findings demonstrate that plasmepsin inhibitor selectivity can be achieved by targeting the flap loop with hydrophobic substituents that enable ligand binding under the flap loop, as such a behavior is not observed for several other aspartic proteases. The ability to estimate the selectivity of compounds before they are synthesized is of considerable importance in drug design; therefore, we expect that our approach will be useful in selective inhibitor designs against not only aspartic proteases but also other enzyme classes.

Prediction of GPCR activity using Machine Learning

GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure–activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%–95%) and also predict the activation state of GPCRs with low error (MAE of 7.15–10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.

Optimization of 2-Amino-4,6-diarylpyrimidine-5-carbonitriles as Potent and Selective A1 Antagonists

We herein document a large collection of 108 2-amino-4,6-disubstituted-pyrimidine derivatives as potent, structurally simple, and highly selective A1AR ligands. The most attractive ligands were confirmed as antagonists of the canonical cyclic adenosine monophosphate pathway, and some pharmacokinetic parameters were preliminarilly evaluated. The library, built through a reliable and efficient three-component reaction, comprehensively explored the chemical space allowing the identification of the most prominent features of the structure-activity and structure-selectivity relationships around this scaffold. These included the influence on the selectivity profile of the aromatic residues at positions R4 and R6 of the pyrimidine core but most importantly the prominent role to the unprecedented A1AR selectivity profile exerted by the methyl group introduced at the exocyclic amino group. The structure-activity relationship trends on both A1 and A2AARs were conveniently interpreted with rigorous free energy perturbation simulations, which started from the receptor-driven docking model that guided the design of these series.

Classification Model for the Second Extracellular Loop of Class A GPCRs

The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Controlling the selectivity of aminergic GPCR ligands from the extracellular vestibule

In addition to the orthosteric binding pocket (OBP) of GPCRs, recent structural studies have revealed that there are several allosteric sites available for pharmacological intervention. The secondary binding pocket (SBP) of aminergic GPCRs is located in the extracellular vestibule of these receptors, and it has been suggested to be a potential selectivity pocket for bitopic ligands. Here, we applied a virtual screening protocol based on fragment docking to the SBP of the orthosteric ligand-receptor complex. This strategy was employed for a number of aminergic receptors. First, we designed dopamine D₃ preferring bitopic compounds from a D₂ selective orthosteric ligand. Next, we designed 5-HT_2B selective bitopic compounds starting from the 5-HT_1B preferring ergoline core of LSD. Comparing the serotonergic profiles of the new derivatives to that of LSD, we found that these derivatives became significantly biased towards the desired 5-HT_2B receptor target. Finally, addressing the known limitations of H₁ antihistamines, our protocol was successfully used to eliminate the well-known side effects related to the muscarinic M₁ activity of amitriptyline while preserving H₁ potency in some of the designed bitopic compounds. These applications highlight the usefulness of our new virtual screening protocol and offer a powerful strategy towards bitopic GPCR ligands with designed receptor profiles.

Development and Application of Subtype-Selective Fluorescent Antagonists for the Study of the Human Adenosine A1 Receptor in Living Cells

The adenosine A₁ receptor (A₁AR) is a G-protein-coupled receptor (GPCR) that provides important therapeutic opportunities for a number of conditions including congestive heart failure, tachycardia, and neuropathic pain. The development of A₁AR-selective fluorescent ligands will enhance our understanding of the subcellular mechanisms underlying A₁AR pharmacology facilitating the development of more efficacious and selective therapies. Herein, we report the design, synthesis, and application of a novel series of A₁AR-selective fluorescent probes based on 8-functionalized bicyclo[2.2.2]octylxanthine and 3-functionalized 8-(adamant-1-yl) xanthine scaffolds. These fluorescent conjugates allowed quantification of kinetic and equilibrium ligand binding parameters using NanoBRET and visualization of specific receptor distribution patterns in living cells by confocal imaging and total internal reflection fluorescence (TIRF) microscopy. As such, the novel A₁AR-selective fluorescent antagonists described herein can be applied in conjunction with a series of fluorescence-based techniques to foster understanding of A₁AR molecular pharmacology and signaling in living cells.

Ligand design by targeting a binding site water

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood. We investigated how altered interactions with a water network can influence ligand binding to a receptor. A series of ligands of the A_2A adenosine receptor, which either interacted with or displaced an ordered binding site water, were studied experimentally and by molecular dynamics simulations. An analog of the endogenous ligand that was unable to hydrogen bond to the ordered water lost affinity and this activity cliff was captured by molecular dynamics simulations. Two compounds designed to displace the ordered water from the binding site were then synthesized and evaluated experimentally, leading to the discovery of an A_2A agonist with nanomolar activity. Calculation of the thermodynamic profiles resulting from introducing substituents that interacted with or displaced the ordered water showed that the gain of binding affinity was enthalpy driven. Detailed analysis of the energetics and binding site hydration networks revealed that the enthalpy change was governed by contributions that are commonly neglected in structure-based drug optimization. In particular, simulations suggested that displacement of water from a binding site to the bulk solvent can lead to large energy contributions. Our findings provide insights into the molecular driving forces of protein–ligand binding and strategies for rational drug design.

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood.

Ligand binding free-energy calculations with funnel metadynamics

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.

Adenosine A2A Receptor Antagonists for Cancer Immunotherapy

Currently, the most promising therapeutic modality for cancer treatment is the blockade of immune checkpoint pathways, which has revolutionized cancer therapy in the past 15 years. Strategies targeting and modulating adenosine A_2A receptor (A_2AR), an emerging alternative immune checkpoint, have shown the potential to produce significant therapeutic effects. In this review, we describe the immunosuppressive activities of A_2AR and A_2BR in the tumor microenvironment (TME), followed by a summary and discussion of the structure-activity relationship (SAR) of the A_2AR (and dual A_2AR/A_2BR) antagonists that have been experimentally confirmed to exert oncoimmunological effects. This review also provides an update on the compounds under clinical evaluation and insights into the ligand binding modes of the receptor.

Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

In Silico Drug Design for Purinergic GPCRs: Overview on Molecular Dynamics Applied to Adenosine and P2Y Receptors

Molecular modeling has contributed to drug discovery for purinergic GPCRs, including adenosine receptors (ARs) and P2Y receptors (P2YRs). Experimental structures and homology modeling have proven to be useful in understanding and predicting structure activity relationships (SAR) of agonists and antagonists. This review provides an excursus on molecular dynamics (MD) simulations applied to ARs and P2YRs. The binding modes of newly synthesized A1AR- and A3AR-selective nucleoside derivatives, potentially of use against depression and inflammation, respectively, have been predicted to recapitulate their SAR and the species dependence of A3AR affinity. P2Y12R and P2Y1R crystallographic structures, respectively, have provided a detailed understanding of the recognition of anti-inflammatory P2Y14R antagonists and a large group of allosteric and orthosteric antagonists of P2Y1R, an antithrombotic and neuroprotective target. MD of A2AAR (an anticancer and neuroprotective target), A3AR, and P2Y1R has identified microswitches that are putatively involved in receptor activation. The approach pathways of different ligands toward A2AAR and P2Y1R binding sites have also been explored. A1AR, A2AAR, and A3AR were utilizes to study allosteric phenomena, but locating the binding site of structurally diverse allosteric modulators, such as an A3AR enhancer LUF6000, is challenging. Ligand residence time, a predictor of in vivo efficacy, and the structural role of water were investigated through A2AAR MD simulations. Thus, new MD and other modeling algorithms have contributed to purinergic GPCR drug discovery.

Quantitative prediction of selectivity between the A1 and A2A adenosine receptors

The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A₁ and A_2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two drug targets by directly training on the difference in bioactivity, modeling the selectivity-window. The quality of this model was good as shown by the performances for fivefold cross-validation: ROC A₁AR-selective 0.88 ± 0.04 and ROC A_2AAR-selective 0.80 ± 0.07. To increase the accuracy of this selectivity model even further, inactive compounds were identified and removed prior to selectivity prediction by a combination of statistical models and structure-based docking. As a result, selectivity between the A₁ and A_2A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A₁ (A₁AR) has been recently solved, providing important structural insights. In the present work, we rationalized the selectivity profile of two selective A₁AR and A_2AAR antagonists, investigating their recognition trajectories obtained by Supervised Molecular Dynamics from an unbound state and monitoring the role of the water molecules in the binding site.

A Taxicab geometry quantification system to evaluate the performance of in silico methods: a case study on adenosine receptors ligands

Among still comparatively few G protein-coupled receptors, the adenosine A_2A receptor has been co-crystallized with several ligands, agonists as well as antagonists. It can thus serve as a template with a well-described orthosteric ligand binding region for adenosine receptors. As not all subtypes have been crystallized yet, and in order to investigate the usability of homology models in this context, multiple adenosine A₁ receptor (A₁AR) homology models had been previously obtained and a library of lead-like compounds had been docked. As a result, a number of potent and one selective ligand toward the intended target have been identified. However, in in vitro experimental verification studies, many ligands also bound to the A_2AAR and the A₃AR subtypes. In this work we asked the question whether a classification of the ligands according to their selectivity was possible based on docking scores. Therefore, we built an A₃AR homology model and docked all previously found ligands to all three receptor subtypes. As a metric, we employed an in vitro/in silico selectivity ranking system based on taxicab geometry and obtained a classification model with reasonable separation. In the next step, the method was validated with an external library of, selective ligands with similarly good performance. This classification system might also be useful in further screens.