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.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

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.

UnCorrupt SMILES: a novel approach to de novo design.

Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89%, with different models having different error-type distributions. Post hoc correction of SMILES was shown to increase model validity. The SMILES corrector trained with one error per input alters 60-90% of invalid generator outputs and fixes 35-80% of them. However, a higher error detection and performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60-95% of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators based on novelty and similarity. Additionally, the SMILES corrector can be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39% and a novelty of approximately 20%. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates.

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.

Cancer-Associated Mutations of the Adenosine A2A Receptor Have Diverse Influences on Ligand Binding and Receptor Functions.

The adenosine A2A receptor (A2AAR) is a class A G-protein-coupled receptor (GPCR). It is an immune checkpoint in the tumor micro-environment and has become an emerging target for cancer treatment. In this study, we aimed to explore the effects of cancer-patient-derived A2AAR mutations on ligand binding and receptor functions. The wild-type A2AAR and 15 mutants identified by Genomic Data Commons (GDC) in human cancers were expressed in HEK293T cells. Firstly, we found that the binding affinity for agonist NECA was decreased in six mutants but increased for the V275A mutant. Mutations A165V and A265V decreased the binding affinity for antagonist ZM241385. Secondly, we found that the potency of NECA (EC50) in an impedance-based cell-morphology assay was mostly correlated with the binding affinity for the different mutants. Moreover, S132L and H278N were found to shift the A2AAR towards the inactive state. Importantly, we found that ZM241385 could not inhibit the activation of V275A and P285L stimulated by NECA. Taken together, the cancer-associated mutations of A2AAR modulated ligand binding and receptor functions. This study provides fundamental insights into the structure-activity relationship of the A2AAR and provides insights for A2AAR-related personalized treatment in cancer.

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.

A2B adenosine receptor antagonists rescue lymphocyte activity in adenosine-producing patient-derived cancer models.

BACKGROUND: Adenosine is a metabolite that suppresses antitumor immune response of T and NK cells via extracellular binding to the two subtypes of adenosine-2 receptors, A2ARs. While blockade of the A2AARs subtype effectively rescues lymphocyte activity, with four A2AAR antagonists currently in anticancer clinical trials, less is known for the therapeutic potential of the other A2BAR blockade within cancer immunotherapy. Recent studies suggest the formation of A2AAR/A2BAR dimers in tissues that coexpress the two receptor subtypes, where the A2BAR plays a dominant role, suggesting it as a promising target for cancer immunotherapy. METHODS: We report the synthesis and functional evaluation of five potent A2BAR antagonists and a dual A2AAR/A2BAR antagonist. The compounds were designed using previous pharmacological data assisted by modeling studies. Synthesis was developed using multicomponent approaches. Flow cytometry was used to evaluate the phenotype of T and NK cells on A2BAR antagonist treatment. Functional activity of T and NK cells was tested in patient-derived tumor spheroid models. RESULTS: We provide data for six novel small molecules: five A2BAR selective antagonists and a dual A2AAR/A2BAR antagonist. The growth of patient-derived breast cancer spheroids is prevented when treated with A2BAR antagonists. To elucidate if this depends on increased lymphocyte activity, immune cells proliferation, and cytokine production, lymphocyte infiltration was evaluated and compared with the potent A2AAR antagonist AZD-4635. We find that A2BAR antagonists rescue T and NK cell proliferation, IFNγ and perforin production, and increase tumor infiltrating lymphocytes infiltration into tumor spheroids without altering the expression of adhesion molecules. CONCLUSIONS: Our results demonstrate that A2BAR is a promising target in immunotherapy, identifying ISAM-R56A as the most potent candidate for A2BAR blockade. Inhibition of A2BAR signaling restores T cell function and proliferation. Furthermore, A2BAR and dual A2AAR/A2BAR antagonists showed similar or better results than A2AAR antagonist AZD-4635 reinforcing the idea of dominant role of the A2BAR in the regulation of the immune system.

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.

Deciphering conformational selectivity in the A2A adenosine G protein-coupled receptor by free energy simulations.

Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A2A adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A2AAR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications.

Potent Inhibition of Nicotinamide N-Methyltransferase by Alkene-Linked Bisubstrate Mimics Bearing Electron Deficient Aromatics.

Nicotinamide N-methyltransferase (NNMT) methylates nicotinamide (vitamin B3) to generate 1-methylnicotinamide (MNA). NNMT overexpression has been linked to a variety of diseases, most prominently human cancers, indicating its potential as a therapeutic target. The development of small-molecule NNMT inhibitors has gained interest in recent years, with the most potent inhibitors sharing structural features based on elements of the nicotinamide substrate and the S-adenosyl-l-methionine (SAM) cofactor. We here report the development of new bisubstrate inhibitors that include electron-deficient aromatic groups to mimic the nicotinamide moiety. In addition, a trans-alkene linker was found to be optimal for connecting the substrate and cofactor mimics in these inhibitors. The most potent NNMT inhibitor identified exhibits an IC50 value of 3.7 nM, placing it among the most active NNMT inhibitors reported to date. Complementary analytical techniques, modeling studies, and cell-based assays provide insights into the binding mode, affinity, and selectivity of these inhibitors.

Identification of V6.51L as a selectivity hotspot in stereoselective A2B adenosine receptor antagonist recognition.

The four adenosine receptors (ARs) A1AR, A2AAR, A2BAR, and A3AR are G protein-coupled receptors (GPCRs) for which an exceptional amount of experimental and structural data is available. Still, limited success has been achieved in getting new chemical modulators on the market. As such, there is a clear interest in the design of novel selective chemical entities for this family of receptors. In this work, we investigate the selective recognition of ISAM-140, a recently reported A2BAR reference antagonist. A combination of semipreparative chiral HPLC, circular dichroism and X-ray crystallography was used to separate and unequivocally assign the configuration of each enantiomer. Subsequently affinity evaluation for both A2A and A2B receptors demonstrate the stereospecific and selective recognition of (S)-ISAM140 to the A2BAR. The molecular modeling suggested that the structural determinants of this selectivity profile would be residue V2506.51 in A2BAR, which is a leucine in all other ARs including the closely related A2AAR. This was herein confirmed by radioligand binding assays and rigorous free energy perturbation (FEP) calculations performed on the L249V6.51 mutant A2AAR receptor. Taken together, this study provides further insights in the binding mode of these A2BAR antagonists, paving the way for future ligand optimization.

Novel Cephalosporin Conjugates Display Potent and Selective Inhibition of Imipenemase-Type Metallo-ß-Lactamases.

In an attempt to exploit the hydrolytic mechanism by which ß-lactamases degrade cephalosporins, we designed and synthesized a series of novel cephalosporin prodrugs aimed at delivering thiol-based inhibitors of metallo-ß-lactamases (MBLs) in a spatiotemporally controlled fashion. While enzymatic hydrolysis of the ß-lactam ring was observed, it was not accompanied by inhibitor release. Nonetheless, the cephalosporin prodrugs, especially thiomandelic acid conjugate (8), demonstrated potent inhibition of IMP-type MBLs. In addition, conjugate 8 was also found to greatly reduce the minimum inhibitory concentration of meropenem against IMP-producing bacteria. The results of kinetic experiments indicate that these prodrugs inhibit IMP-type MBLs by acting as slowly turned-over substrates. Structure-activity relationship studies revealed that both phenyl and carboxyl moieties of 8 are crucial for its potency. Furthermore, modeling studies indicate that productive interactions of the thiomandelic acid moiety of 8 with Trp28 within the IMP active site may contribute to its potency and selectivity.

Potent and Subtype-Selective Dopamine D2 Receptor Biased Partial Agonists Discovered via an Ugi-Based Approach.

Using a previously unexplored, efficient, and versatile multicomponent method, we herein report the rapid generation of novel potent and subtype-selective DRD2 biased partial agonists. This strategy exemplifies the search for diverse and previously unexplored moieties for the secondary/allosteric pharmacophore of the common phenyl-piperazine scaffold. The pharmacological characterization of the new compound series led to the identification of several ligands with excellent DRD2 affinity and subtype selectivity and remarkable functional selectivity for either the cAMP (22a and 24d) or the ß-arrestin (27a and 29c) signaling pathways. These results were further interpreted on the basis of molecular models of these ligands in complex with the recent DRD2 crystal structures, highlighting the critical role of the secondary/allosteric pharmacophore in modulating the functional selectivity profile.

Free Energy Calculations for Protein-Ligand Binding Prediction.

Computational prediction of protein-ligand binding involves initial determination of the binding mode and subsequent evaluation of the strength of the protein-ligand interactions, which directly correlates with ligand binding affinities. As a consequence of increasing computer power, rigorous approaches to calculate protein-ligand binding affinities, such as free energy perturbation (FEP) methods, are becoming an essential part of the toolbox of computer-aided drug design. In this chapter, we provide a general overview of these methods and introduce the QFEP modules, which are open-source API workflows based on our molecular dynamics (MD) package Q. The module QligFEP allows estimation of relative binding affinities along ligand series, while QresFEP is a module to estimate binding affinity shifts caused by single-point mutations of the protein. We herein provide guidelines for the use of each of these modules based on data extracted from ligand-design projects. While these modules are stand-alone, the combined use of the two workflows in a drug-design project yields complementary perspectives of the ligand binding problem, providing two sides of the same coin. The selected case studies illustrate how to use QFEP to approach the two key questions associated with ligand binding prediction: identifying the most favorable binding mode from different alternatives and establishing structure-affinity relationships that allow the rational optimization of hit compounds.

3,4-Dihydropyrimidin-2(1H)-ones as Antagonists of the Human A2B Adenosine Receptor: Optimization, Structure-Activity Relationship Studies, and Enantiospecific Recognition.

We present and thoroughly characterize a large collection of 3,4-dihydropyrimidin-2(1H)-ones as A2BAR antagonists, an emerging strategy in cancer (immuno) therapy. Most compounds selectively bind A2BAR, with a number of potent and selective antagonists further confirmed by functional cyclic adenosine monophosphate experiments. The series was analyzed with one of the most exhaustive free energy perturbation studies on a GPCR, obtaining an accurate model of the structure-activity relationship of this chemotype. The stereospecific binding modeled for this scaffold was confirmed by resolving the two most potent ligands [(±)-47, and (±)-38 Ki = 10.20 and 23.6 nM, respectively] into their two enantiomers, isolating the affinity on the corresponding (S)-eutomers (Ki = 6.30 and 11.10 nM, respectively). The assessment of the effect in representative cytochromes (CYP3A4 and CYP2D6) demonstrated insignificant inhibitory activity, while in vitro experiments in three prostate cancer cells demonstrated that this pair of compounds exhibits a pronounced antimetastatic effect.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor Antagonists.

A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A2BAR antagonists has been carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates, which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identification of new ligands that combine remarkable affinity (Ki < 30 nM) and exquisite selectivity. The structure-activity relationship (SAR) trends identified were substantiated by a molecular modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity, respectively. The stereospecific interaction between hA2BAR and the eutomer of the most attractive novel antagonist (S)-18g (Ki = 3.66 nM) was validated.