Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities.

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Pharmacological characterization of allosteric modulators: A case for chemokine receptors.

Chemokine receptors are relevant targets for a multitude of immunological diseases, but drug attrition for these receptors is remarkably high. While many drug discovery programs have been pursued, most prospective drugs failed in the follow-up studies due to clinical inefficacy, and hence there is a clear need for alternative approaches. Allosteric modulators of receptor function represent an excellent opportunity for novel drugs, as they modulate receptor activation in a controlled manner and display increased selectivity, and their pharmacological profile can be insurmountable. Here, we discuss allosteric ligands and their pharmacological characterization for modulation of chemokine receptors. Ligands are included if (1) they show clear signs of allosteric modulation in vitro and (2) display evidence of binding in a topologically distinct manner compared to endogenous chemokines. We discuss how allosteric ligands affect binding of orthosteric (endogenous) ligands in terms of affinity as well as binding kinetics in radioligand binding assays. Moreover, their effects on signaling events in functional assays and how their binding site can be elucidated are specified. We substantiate this with examples of published allosteric ligands targeting chemokine receptors and hypothetical graphs of pharmacological behavior. This review should serve as an effective starting point for setting up assays for characterizing allosteric ligands to develop safer and more efficacious drugs for chemokine receptors and, ultimately, other G protein-coupled receptors.

Small-molecule CEM3 strengthens single-cell oscillators in the suprachiasmatic nucleus.

A robust endogenous clock is required for proper function of many physiological processes. The suprachiasmatic nucleus (SCN) constitutes our central circadian clock and allows us to adapt to daily changes in the environment. Aging can cause a decline in the amplitude of circadian rhythms in SCN and peripheral clocks, which contributes to increased risk of several chronic diseases. Strengthening clock function would therefore be an effective strategy to improve health. A high-throughput chemical screening has identified clock-enhancing molecule 3 (CEM3) as small molecule that increases circadian rhythm amplitude in cell lines and SCN explants. It is, however, currently not known whether CEM3 acts by enhancing the amplitude of individual single-cell oscillators or by enhancing synchrony among neurons. In view of CEM3's potential, it is of evident importance to clarify the mode of action of CEM3. Here, we investigated the effects of CEM3 on single-cell PERIOD2::LUCIFERASE rhythms in mouse SCN explants. CEM3 increased the amplitude in approximately 80%-90% of the individual cells in the SCN without disrupting the phase and/or period of their rhythms. Noticeably, CEM3's effect on amplitude is independent of the cell's initial amplitude. These findings make CEM3 a potential therapeutic candidate to restore compromised amplitude in circadian rhythms and will boost the development of other molecular approaches to improve health.

Molecular insights into disease-associated glutamate transporter (EAAT1 / SLC1A3) variants using in silico and in vitro approaches.

Glutamate is an essential excitatory neurotransmitter and an intermediate for energy metabolism. Depending on the tumor site, cancer cells have increased or decreased expression of excitatory amino acid transporter 1 or 2 (EAAT1/2, SLC1A3/2) to regulate glutamate uptake for the benefit of tumor growth. Thus, EAAT1/2 may be an attractive target for therapeutic intervention in oncology. Genetic variation of EAAT1 has been associated with rare cases of episodic ataxia, but the occurrence and functional contribution of EAAT1 mutants in other diseases, such as cancer, is poorly understood. Here, 105 unique somatic EAAT1 mutations were identified in cancer patients from the Genomic Data Commons dataset. Using EAAT1 crystal structures and in silico studies, eight mutations were selected based on their close proximity to the orthosteric or allosteric ligand binding sites and the predicted change in ligand binding affinity. In vitro functional assessment in a live-cell, impedance-based phenotypic assay demonstrated that these mutants differentially affect L-glutamate and L-aspartate transport, as well as the inhibitory potency of an orthosteric (TFB-TBOA) and allosteric (UCPH-101) inhibitor. Moreover, two episodic ataxia-related mutants displayed functional responses that were in line with literature, which confirmed the validity of our assay. Of note, ataxia-related mutant M128R displayed inhibitor-induced functional responses never described before. Finally, molecular dynamics (MD) simulations were performed to gain mechanistic insights into the observed functional effects. Taken together, the results in this work demonstrate 1) the suitability of the label-free phenotypic method to assess functional variation of EAAT1 mutants and 2) the opportunity and challenges of using in silico techniques to rationalize the in vitro phenotype of disease-relevant mutants.

The translational value of ligand-receptor binding kinetics in drug discovery.

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50 /IC50 and KD /Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug-target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug-target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much-needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.

Development of an Affinity-Based Probe to Profile Endogenous Human Adenosine A3 Receptor Expression.

The adenosine A3 receptor (A3AR) is a G protein-coupled receptor (GPCR) that exerts immunomodulatory effects in pathophysiological conditions such as inflammation and cancer. Thus far, studies toward the downstream effects of A3AR activation have yielded contradictory results, thereby motivating the need for further investigations. Various chemical and biological tools have been developed for this purpose, ranging from fluorescent ligands to antibodies. Nevertheless, these probes are limited by their reversible mode of binding, relatively large size, and often low specificity. Therefore, in this work, we have developed a clickable and covalent affinity-based probe (AfBP) to target the human A3AR. Herein, we show validation of the synthesized AfBP in radioligand displacement, SDS-PAGE, and confocal microscopy experiments as well as utilization of the AfBP for the detection of endogenous A3AR expression in flow cytometry experiments. Ultimately, this AfBP will aid future studies toward the expression and function of the A3AR in pathologies.

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors.

Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.

Kinetic profiling of novel spirobenzo-oxazinepiperidinone derivatives as equilibrative nucleoside transporter 1 inhibitors.

Evaluation of kinetic parameters of drug-target binding, kon, koff, and residence time (RT), in addition to the traditional in vitro parameter of affinity is receiving increasing attention in the early stages of drug discovery. Target binding kinetics emerges as a meaningful concept for the evaluation of a ligand's duration of action and more generally drug efficacy and safety. We report the biological evaluation of a novel series of spirobenzo-oxazinepiperidinone derivatives as inhibitors of the human equilibrative nucleoside transporter 1 (hENT1, SLC29A1). The compounds were evaluated in radioligand binding experiments, i.e., displacement, competition association, and washout assays, to evaluate their affinity and binding kinetic parameters. We also linked these pharmacological parameters to the compounds' chemical characteristics, and learned that separate moieties of the molecules governed target affinity and binding kinetics. Among the 29 compounds tested, 28 stood out with high affinity and a long residence time of 87 min. These findings reveal the importance of supplementing affinity data with binding kinetics at transport proteins such as hENT1.

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors.

Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning.

Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a  Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from  a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A2A receptor (A2AAR) and compared with SMILES-based methods. The results show that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A2AAR with given scaffolds.

A Chemical Biological Approach to Study G Protein-Coupled Receptors: Labeling the Adenosine A1 Receptor Using an Electrophilic Covalent Probe.

G protein-coupled receptors (GPCRs) have been known for decades as attractive drug targets. This has led to the development and approval of many ligands targeting GPCRs. Although ligand binding effects have been studied thoroughly for many GPCRs, there are multiple aspects of GPCR signaling that remain poorly understood. The reasons for this are the difficulties that are encountered upon studying GPCRs, for example, a poor solubility and low expression levels. In this work, we have managed to overcome some of these issues by developing an affinity-based probe for a prototypic GPCR, the adenosine A1 receptor (A1AR). Here, we show the design, synthesis, and biological evaluation of this probe in various biochemical assays, such as SDS-PAGE, confocal microscopy, and chemical proteomics.

Development of subtype-selective covalent ligands for the adenosine A2B receptor by tuning the reactive group.

Signalling through the adenosine receptors (ARs), in particular through the adenosine A2B receptor (A2BAR), has been shown to play a role in a variety of pathological conditions, ranging from immune disorders to cancer. Covalent ligands for the A2BAR have the potential to irreversibly block the receptor, as well as inhibit all A2BAR-induced signalling pathways. This will allow a thorough investigation of the pathophysiological role of the receptor. In this study, we synthesized and evaluated a set of potential covalent ligands for the A2BAR. The ligands all contain a core scaffold consisting of a substituted xanthine, varying in type and orientation of electrophilic group (warhead). Here, we find that the right combination of these variables is necessary for a high affinity, irreversible mode of binding and selectivity towards the A2BAR. Altogether, this is the case for sulfonyl fluoride 24 (LUF7982), a covalent ligand that allows for novel ways to interrogate the A2BAR.

Impedance-Based Phenotypic Readout of Transporter Function: A Case for Glutamate Transporters.

Excitatory amino acid transporters (EAAT/SLC1) mediate Na+-dependent uptake of extracellular glutamate and are potential drug targets for neurological disorders. Conventional methods to assess glutamate transport in vitro are based on radiolabels, fluorescent dyes or electrophysiology, which potentially compromise the cell's physiology and are generally less suited for primary drug screens. Here, we describe a novel label-free method to assess human EAAT function in living cells, i.e., without the use of chemical modifications to the substrate or cellular environment. In adherent HEK293 cells overexpressing EAAT1, stimulation with glutamate or aspartate induced cell spreading, which was detected in real-time using an impedance-based biosensor. This change in cell morphology was prevented in the presence of the Na+/K+-ATPase inhibitor ouabain and EAAT inhibitors, which suggests the substrate-induced response was ion-dependent and transporter-specific. A mechanistic explanation for the phenotypic response was substantiated by actin cytoskeleton remodeling and changes in the intracellular levels of the osmolyte taurine, which suggests that the response involves cell swelling. In addition, substrate-induced cellular responses were observed for cells expressing other EAAT subtypes, as well as in a breast cancer cell line (MDA-MB-468) with endogenous EAAT1 expression. These findings allowed the development of a label-free high-throughput screening assay, which could be beneficial in early drug discovery for EAATs and holds potential for the study of other transport proteins that modulate cell shape.

Cancer-Related Somatic Mutations in Transmembrane Helices Alter Adenosine A1 Receptor Pharmacology.

Overexpression of the adenosine A1 receptor (A1AR) has been detected in various cancer cell lines. However, the role of A1AR in tumor development is still unclear. Thirteen A1AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. We have investigated the pharmacology of the mutations located at the 7-transmembrane domain using a yeast system. Concentration-growth curves were obtained with the full agonist CPA and compared to the wild type hA1AR. H78L3.23 and S246T6.47 showed increased constitutive activity, while only the constitutive activity of S246T6.47 could be reduced to wild type levels by the inverse agonist DPCPX. Decreased constitutive activity was observed on five mutant receptors, among which A52V2.47 and W188C5.46 showed a diminished potency for CPA. Lastly, a complete loss of activation was observed in five mutant receptors. A selection of mutations was also investigated in a mammalian system, showing comparable effects on receptor activation as in the yeast system, except for residues pointing toward the membrane. Taken together, this study will enrich the view of the receptor structure and function of A1AR, enlightening the consequences of these mutations in cancer. Ultimately, this may provide an opportunity for precision medicine for cancer patients with pathological phenotypes involving these mutations.

Cancer-related somatic mutations alter adenosine A1 receptor pharmacology-A focus on mutations in the loops and C-terminus.

G protein-coupled receptors (GPCRs) are known to be involved in tumor progression and metastasis. The adenosine A1 receptor (A1 AR) has been detected to be over-expressed in various cancer cell lines. However, the role of A1 AR in tumor development is not yet well characterized. A series of A1 AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. In this study, we have investigated the pharmacology of mutations located outside of the 7-transmembrane domain by using a "single-GPCR-one-G protein" yeast system. Concentration-growth curves were obtained with the full agonist CPA for 12 mutant receptors and compared to the wild-type hA1 AR. Most mutations located at the extracellular loops (EL) reduced the levels of constitutive activity of the receptor and agonist potency. For mutants at the intracellular loops (ILs) of the receptor, an increased constitutive activity was found for mutant receptor L211R5.69 , while a decreased constitutive activity and agonist response were found for mutant receptor L113F34.51 . Lastly, mutations identified on the C-terminus did not significantly influence the pharmacological function of the receptor. A selection of mutations was also investigated in a mammalian system. Overall, similar effects on receptor activation compared to the yeast system were found with mutations located at the EL, but some contradictory effects were observed for mutations located at the IL. Taken together, this study will enrich the insight of A1 AR structure and function, enlightening the consequences of these mutations in cancer. Ultimately, this may provide potential precision medicine in cancer treatment.

Kinetic profiling and functional characterization of 8-phenylxanthine derivatives as A2B adenosine receptor antagonists.

A2B adenosine receptor (A2BAR) antagonists have therapeutic potential in inflammation-related diseases such as asthma, chronic obstructive pulmonary disease and cancer. However, no drug is currently clinically approved, creating a demand for research on novel antagonists. Over the last decade, the study of target binding kinetics, along with affinity and potency, has been proven valuable in early drug discovery stages, as it is associated with improved in vivo drug efficacy and safety. In this study, we report the synthesis and biological evaluation of a series of xanthine derivatives as A2BAR antagonists, including an isothiocyanate derivative designed to bind covalently to the receptor. All 28 final compounds were assessed in radioligand binding experiments, to evaluate their affinity and for those qualifying, kinetic binding parameters. Both structure-affinity and structure-kinetic relationships were derived, providing a clear relationship between affinity and dissociation rate constants. Two structurally similar compounds, 17 and 18, were further evaluated in a label-free assay due to their divergent kinetic profiles. An extended cellular response was associated with long A2BAR residence times. This link between a ligand's A2BAR residence time and its functional effect highlights the importance of binding kinetics as a selection parameter in the early stages of drug discovery.

International Union of Basic and Clinical Pharmacology. CXII: Adenosine Receptors: A Further Update.

Our previous International Union of Basic and Clinical Pharmacology report on the nomenclature and classification of adenosine receptors (2011) contained a number of emerging developments with respect to this G protein-coupled receptor subfamily, including protein structure, protein oligomerization, protein diversity, and allosteric modulation by small molecules. Since then, a wealth of new data and results has been added, allowing us to explore novel concepts such as target binding kinetics and biased signaling of adenosine receptors, to examine a multitude of receptor structures and novel ligands, to gauge new pharmacology, and to evaluate clinical trials with adenosine receptor ligands. This review should therefore be considered a further update of our previous reports from 2001 and 2011. SIGNIFICANCE STATEMENT: Adenosine receptors (ARs) are of continuing interest for future treatment of chronic and acute disease conditions, including inflammatory diseases, neurodegenerative afflictions, and cancer. The design of AR agonists ("biased" or not) and antagonists is largely structure based now, thanks to the tremendous progress in AR structural biology. The A2A- and A2BAR appear to modulate the immune response in tumor biology. Many clinical trials for this indication are ongoing, whereas an A2AAR antagonist (istradefylline) has been approved as an anti-Parkinson agent.

Oncological drug discovery: AI meets structure-based computational research.

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Targeting solute carriers to modulate receptor-ligand interactions.

Solute carrier transporters (SLCs) limit receptor activation via uptake of extracellular ligands. Novel concepts are emerging that describe the modulation of intracellular and plasma membrane receptors by ligand influx and efflux via SLCs, respectively. Here, we evaluate recent insights and provide an outlook for developing potential therapeutic strategies.

MPP+-Induced Changes in Cellular Impedance as a Measure for Organic Cation Transporter (SLC22A1-3) Activity and Inhibition.

The organic cation transporters OCT1-3 (SLC22A1-3) facilitate the transport of cationic endo- and xenobiotics and are important mediators of drug distribution and elimination. Their polyspecific nature makes OCTs highly susceptible to drug-drug interactions (DDIs). Currently, screening of OCT inhibitors depends on uptake assays that require labeled substrates to detect transport activity. However, these uptake assays have several limitations. Hence, there is a need to develop novel assays to study OCT activity in a physiological relevant environment without the need to label the substrate. Here, a label-free impedance-based transport assay is established that detects OCT-mediated transport activity and inhibition utilizing the neurotoxin MPP+. Uptake of MPP+ by OCTs induced concentration-dependent changes in cellular impedance that were inhibited by decynium-22, corticosterone, and Tyrosine Kinase inhibitors. OCT-mediated MPP+ transport activity and inhibition were quantified on both OCT1-3 overexpressing cells and HeLa cells endogenously expressing OCT3. Moreover, the method presented here is a valuable tool to identify novel inhibitors and potential DDI partners for MPP+ transporting solute carrier proteins (SLCs) in general.