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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-HT2B selective bitopic compounds starting from the 5-HT1B 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-HT2B 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.

Background Vanishing white matter (VWM) is a leukodystrophy, caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B)‐subunit genes (EIF2B1–EIF2B5); 80% are missense mutations. Clinical severity is highly variable, with a strong, unexplained genotype–phenotype correlation. Materials and Methods With information from a recent natural history study, we severity‐graded 97 missense mutations. Using in silico modeling, we created a new human eIF2B model structure, onto which we mapped the missense mutations. Mutated residues were assessed for location in subunits, eIF2B complex, and functional domains, and for information on biochemical activity. Results Over 50% of mutations have (ultra‐)severe phenotypic effects. About 60% affect the ε‐subunit, containing the catalytic domain, mostly with (ultra‐)severe effects. About 55% affect subunit cores, with variable clinical severity. About 36% affect subunit interfaces, mostly with severe effects. Very few mutations occur on the external eIf2B surface, perhaps because they have minor functional effects and are tolerated. One external surface mutation affects eIF2B‐substrate interaction and is associated with ultra‐severe phenotype. Conclusion Mutations that lead to (ultra‐)severe disease mostly affect amino acids with pivotal roles in complex formation and function of eIF2B. Therapies for VWM are emerging and reliable mutation‐based phenotype prediction is required for propensity score matching for trials and in the future for individualized therapy decisions., Vanishing white matter (VWM) is a clinically highly variable leukodystrophy, caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B)‐subunit genes, mostly missense mutations. To gain insight into the strong genotype‐phenotype correlation, we severity‐graded 97 missense mutations using information from a clinical natural history study, created a new human eIF2B model structure by in silico modeling, and assessed mutated residues for location in subunits, eIF2B complex, and functional domains, and for information on biochemical activity. We demonstrated that mutations associated with (ultra‐)severe disease mostly affect amino acids with pivotal roles in complex formation and eIF2B function, while mutations on the external eIf2B surface are mostly associated with mild phenotypes, probably because of minor functional effects. Therapies for VWM are emerging and reliable mutation‐based phenotype prediction is required for propensity score matching for trials and individualized therapy decisions.

Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS—Kinase–Ligand Interaction Fingerprints and Structures—is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X‐ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2AAR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2AAR were experimentally determined and investigated through a cycle of ligand‐FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X‐ray crystallography of the A2AAR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2AAR, an emerging target in immuno‐oncology., In molecular design, structural, pharmacological and chemical information can be interconnected by computational estimations of binding free energies. Using a combined FEP approach to examine both mutagenesis and ligand SAR, we designed new analogues of the A2AAR antagonist series of chromones. Subsequent crystal structures supported the rational design of these compounds, linking the structural and energetic understanding on ligand binding.

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

Dysregulation of peptide-activated pathways causes a range of diseases, fostering the discovery and clinical development of peptide drugs. Many endogenous peptides activate G protein-coupled receptors (GPCRs) — nearly 50 GPCR peptide drugs have been approved to date, most of them for metabolic disease or oncology, and more than 10 potentially first-in-class peptide therapeutics are in the pipeline. The majority of existing peptide therapeutics are agonists, which reflects the currently dominant strategy of modifying the endogenous peptide sequence of ligands for peptide-binding GPCRs. Increasingly, novel strategies are being employed to develop both agonists and antagonists, to both introduce chemical novelty and improve drug-like properties. Pharmacodynamic improvements are evolving to allow biasing ligands to activate specific downstream signalling pathways, in order to optimize efficacy and reduce side effects. In pharmacokinetics, modifications that increase plasma half-life have been revolutionary. Here, we discuss the current status of the peptide drugs targeting GPCRs, with a focus on evolving strategies to improve pharmacokinetic and pharmacodynamic properties. Many G protein-coupled receptors (GPCRs) have endogenous peptide agonists, and modifying the sequence of these peptides has led to some successful therapeutics. In this Review, Davenport and colleagues discuss strategies to generate effective GPCR-targeted peptide therapeutics by introducing chemical novelty, extending plasma half-life, improving a therapeutic’s drug-like properties or generating biased ligands. These approaches could overcome some of the challenges in developing peptide therapeutics.

Current therapies for Alzheimer's disease seek to correct for defective cholinergic transmission by preventing the breakdown of acetylcholine through inhibition of acetylcholinesterase, these however have limited clinical efficacy. An alternative approach is to directly activate cholinergic receptors responsible for learning and memory. The M1-muscarinic acetylcholine (M1) receptor is the target of choice but has been hampered by adverse effects. Here we aimed to design the drug properties needed for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissue-based assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans. Through this approach, we rationally designed the optimal properties, including selectivity and partial agonism, into HTL9936-a potential candidate for the treatment of memory loss in Alzheimer's disease. More broadly, this demonstrates a strategy for targeting difficult GPCR targets from structure to clinic.

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

The two G protein-coupled receptors (GPCRs) C-X-C chemokine receptor type 4 (CXCR4) and atypical chemokine receptor 3 (ACKR3) are part of the class A chemokine GPCR family and represent important drug targets for human immunodeficiency virus (HIV) infection, cancer, and inflammation diseases. CXCR4 is one of only three chemokine receptors with a US Food and Drug Administration approved therapeutic agent, the small-molecule modulator AMD3100. In this review, known modulators of the two receptors are discussed in detail. Initially, the structural relationship between receptors and ligands is reviewed on the basis of common structural motifs and available crystal structures. To date, no atypical chemokine receptor has been crystallized, which makes ligand design and predictions for these receptors more difficult. Next, the selectivity, receptor activation, and the resulting ligand-induced signaling output of chemokines and other peptide ligands are reviewed. Binding of pepducins, a class of lipid-peptides whose basis is the internal loop of a GPCR, to CXCR4 is also discussed. Finally, small-molecule modulators of CXCR4 and ACKR3 are reviewed. These modulators have led to the development of radio- and fluorescently labeled tool compounds, enabling the visualization of ligand binding and receptor characterization both in vitro and in vivo. SIGNIFICANCE STATEMENT To investigate the pharmacological modulation of CXCR4 and ACKR3, significant effort has been focused on the discovery and development of a range of ligands, including small-molecule modulators, pepducins, and synthetic peptides. Imaging tools, such as fluorescent probes, also play a pivotal role in the field of drug discovery. This review aims to provide an overview of the aforementioned modulators that facilitate the study of CXCR4 and ACKR3 receptors.

Drug-target binding kinetics are an important predictor of in vivo drug efficacy, yet the relationship between ligand structures and their binding kinetics is often poorly understood. We show that both rupatadine (1) and desloratadine (2) have a long residence time at the histamine H1 receptor (H1R). Through development of a [3H]levocetirizine radiolabel, we find that the residence time of 1 exceeds that of 2 more than 10-fold. This was further explored with 22 synthesized rupatadine and desloratadine analogues. Methylene-linked cycloaliphatic or β-branched substitutions of desloratadine increase the residence time at the H1R, conveying a longer duration of receptor antagonism. However, cycloaliphatic substituents directly attached to the piperidine amine (i.e., lacking the spacer) have decreased binding affinity and residence time compared to their methylene-linked structural analogues. Guided by docking studies, steric constraints within the binding pocket are hypothesized to explain the observed differences in affinity and binding kinetics between analogues.

Adenosine receptors are involved in many pathological conditions and are thus promising drug targets. However, developing drugs that target this GPCR subfamily is a challenging task. A number of drug candidates fail due to lack of selectivity which results in unwanted side effects. The extensive structural similarity of adenosine receptors complicates the design of selective ligands. The problem of selective targeting is a general concern in GPCRs, and in this respect adenosine receptors are a prototypical example. Here we use enhanced sampling simulations to decipher the determinants of selectivity of ligands in A2a and A1 adenosine receptors. Our model shows how small differences in the binding pocket and in the water network around the ligand can be leveraged to achieve selectivity.

The adhesion class B2 family of G protein-coupled receptors (GPCRs) plays a key role in important physiological processes and their dysfunction is linked to brain malformations and tumorigenesis. Although information regarding their signaling properties is starting to emerge, the structural motifs and interaction networks that determine 7 transmembrane (TM) signaling of class B2 GPCRs remain to be elucidated. Comparative sequence-structure analyses of class B2 GPCRs and the recently solved active class B1 structures show that class B2 GPCRs include different elements of the conserved residue motifs that determine class B1 activation. Combined site-directed mutagenesis and molecular dynamics studies were performed to give detailed insights into the role of 7TM interaction networks in signaling of two representative class B2 receptors, ADGRG1 (GPR56) and ADGRL4 (ELTD1). The systematic investigation of class B1/B2 sequence motifs provides consistent structure-function relationships that can be translated to the whole class B2 GPCR family and suggests that class B1 and B2 GPCRs share conserved intramolecular 7TM interactions. This improved understanding in adhesion GPCR structure and constitutive signaling can accelerate drug design campaigns for this chemically unexplored receptor class.

Allosteric modulators have emerged with many potential pharmacological advantages as they do not compete the binding of agonist or antagonist to the orthosteric sites but ultimately affect downstream signaling. To identify allosteric modulators targeting an extra-helical binding site of the glucagon-like peptide-1 receptor (GLP-1R) within the membrane environment, the following two computational approaches were applied: structure-based virtual screening with consideration of lipid contacts and ligand-based virtual screening with the maintenance of specific allosteric pocket residue interactions. Verified by radiolabeled ligand binding and cAMP accumulation experiments, two negative allosteric modulators and seven positive allosteric modulators were discovered using structure-based and ligand-based virtual screening methods, respectively. The computational approach presented here could possibly be used to discover allosteric modulators of other G protein-coupled receptors.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based models often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide – a structure-based approach – as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic -resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the threedimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. Significance Statement--Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Significance Class A G protein−coupled receptors (GPCRs) can form dimers and oligomers via poorly understood mechanisms. We show here that the chemokine receptor CXCR4, which is a major pharmacological target, has an oligomerization behavior modulated by its active conformation. Combining advanced, single-molecule, and single-cell optical tools with functional assays and computational approaches, we unveil three key features of CXCR4 quaternary organization: CXCR4 dimerization 1) is dynamic, 2) increases with receptor expression level, and 3) can be disrupted by stabilizing an inactive receptor conformation. Ligand binding motifs reveal a ligand binding subpocket essential to modulate both CXCR4 basal activity and dimerization. This is relevant to develop new strategies to design CXCR4-targeting drugs., Although class A G protein−coupled receptors (GPCRs) can function as monomers, many of them form dimers and oligomers, but the mechanisms and functional relevance of such oligomerization is ill understood. Here, we investigate this problem for the CXC chemokine receptor 4 (CXCR4), a GPCR that regulates immune and hematopoietic cell trafficking, and a major drug target in cancer therapy. We combine single-molecule microscopy and fluorescence fluctuation spectroscopy to investigate CXCR4 membrane organization in living cells at densities ranging from a few molecules to hundreds of molecules per square micrometer of the plasma membrane. We observe that CXCR4 forms dynamic, transient homodimers, and that the monomer−dimer equilibrium is governed by receptor density. CXCR4 inverse agonists that bind to the receptor minor pocket inhibit CXCR4 constitutive activity and abolish receptor dimerization. A mutation in the minor binding pocket reduced the dimer-disrupting ability of these ligands. In addition, mutating critical residues in the sixth transmembrane helix of CXCR4 markedly diminished both basal activity and dimerization, supporting the notion that CXCR4 basal activity is required for dimer formation. Together, these results link CXCR4 dimerization to its density and to its activity. They further suggest that inverse agonists binding to the minor pocket suppress both dimerization and constitutive activity and may represent a specific strategy to target CXCR4.

Current therapies for Alzheimer's disease seek to correct for defective cholinergic transmission by preventing the breakdown of acetylcholine through inhibition of acetylcholinesterase, these however have limited clinical efficacy. An alternative approach is to directly activate cholinergic receptors responsible for learning and memory. The M1-muscarinic acetylcholine (M1) receptor is the target of choice but has been hampered by adverse effects. Here we aimed to design the drug properties needed for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissue-based assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans. Through this approach, we rationally designed the optimal properties, including selectivity and partial agonism, into HTL9936-a potential candidate for the treatment of memory loss in Alzheimer's disease. More broadly, this demonstrates a strategy for targeting difficult GPCR targets from structure to clinic.

Structures of 70 unique G protein-coupled receptors (GPCRs) have been determined, with over 370 structures in total bound to different ligands and the receptors in various conformational states. Structure-based drug design has been applied to an increasing number of GPCR targets over the past decade and now a few of these drug candidates have entered clinical trials. Given the length of time required for a drug to reach the market, there are no documented examples of licensed drugs being developed with the aid of a structure, but this is likely to change as current efforts come to fruition.

The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

Identifying desired interactions with a target receptor is often the first step when designing new active compounds. However, attention should also be focused on contacts with other proteins that result in either selective or polypharmacological compounds. Here, the search for the structural determinants of selectivity between selected serotonin receptor subtypes was carried out. Special attention was focused on 5-HT7R and the cross-interactions between its ligands and the 5-HT1AR, 5-HT1BR, 5-HT2AR, 5-HT2BR, and 5-HT6R subtypes. Selective and non-selective compounds for each pair of 5-HT7/5-HTx receptors were docked to the respective 5-HTR homology models and 5-HT1B/5-HT2BR crystal structures. The contacts present in the ligand-receptor complexes obtained by docking were characterized by the structural interaction fingerprint and statistically analyzed in terms of their frequency. The results allowed for the identification of amino acids that discriminate between selective and non-selective compounds for each 5-HT7/5-HTx receptor pair, which was further compared with available mutagenesis data. Interaction pattern characteristics for compounds with particular activity profiles can constitute the basis for the coherent selectivity theory within a considered set of proteins, supporting the ongoing development of new ligands targeting these receptors. The in silico results were used to analyze prospective virtual screening results towards the 5-HT7 receptor in which compounds of different chemotypes to known 5-HT7R ligands, with micromolar level activities were identified. The findings in this study not only confirm the legitimacy of the approach but also constitute a great starting point for further studies on 5-HT7R ligands selectivity.

The human glucagon receptor, GCGR, belongs to the class B G-protein-coupled receptor family and plays a key role in glucose homeostasis and the pathophysiology of type 2 diabetes. Here we report the 3.0 Å crystal structure of full-length GCGR containing both the extracellular domain and transmembrane domain in an inactive conformation. The two domains are connected by a 12-residue segment termed the stalk, which adopts a β-strand conformation, instead of forming an α-helix as observed in the previously solved structure of the GCGR transmembrane domain. The first extracellular loop exhibits a β-hairpin conformation and interacts with the stalk to form a compact β-sheet structure. Hydrogen-deuterium exchange, disulfide crosslinking and molecular dynamics studies suggest that the stalk and the first extracellular loop have critical roles in modulating peptide ligand binding and receptor activation. These insights into the full-length GCGR structure deepen our understanding of the signalling mechanisms of class B G-protein-coupled receptors.