Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Cytotoxic Potential of Alternaria tenuissima AUMC14342 Mycoendophyte Extract: A Study Combined with LC-MS/MS Metabolic Profiling and Molecular Docking Simulation

Breast, cervical, and ovarian cancers are among the most serious cancers and the main causes of mortality in females worldwide, necessitating urgent efforts to find newer sources of safe anticancer drugs. The present study aimed to evaluate the anticancer potency of mycoendophytic Alternaria tenuissima AUMC14342 ethyl acetate extract on HeLa (cervical cancer), SKOV-3 (ovarian cancer), and MCF-7 (breast adenocarcinoma) cell lines. The extract showed potent effect on MCF-7 cells with an IC50 value of 55.53 μg/mL. Cell cycle distribution analysis of treated MCF-7 cells revealed a cell cycle arrest at the S phase with a significant increase in the cell population (25.53%). When compared to control cells, no significant signs of necrotic or apoptotic cell death were observed. LC-MS/MS analysis of Alternaria tenuissima extract afforded the identification of 20 secondary metabolites, including 7-dehydrobrefeldin A, which exhibited the highest interaction score (−8.0156 kcal/mol) in molecular docking analysis against human aromatase. Regarding ADME pharmacokinetics and drug-likeness properties, 7-dehydrobrefeldin A, 4’-epialtenuene, and atransfusarin had good GIT absorption and water solubility without any violation of drug-likeness rules. These findings support the anticancer activity of bioactive metabolites derived from endophytic fungi and provide drug scaffolds and substitute sources for the future development of safe chemotherapy.

Ligand-Based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

BACKGROUND

Chemokines are involved in several human diseases and in different stages of COVID-19 infection and play critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

METHODS

We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM.

RESULTS AND CONCLUSION

These results prove the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases towards new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

L-type prostaglandin D synthase regulates the trafficking of the PGD2 DP1 receptor by interacting with the GTPase Rab4

Accumulating evidence indicates that G protein-coupled receptors (GPCRs) interact with Rab GTPases during their intracellular trafficking. How GPCRs recruit and activate the Rabs is unclear. Here, we report that depletion of endogenous L-type prostaglandin D synthase (L-PGDS) in HeLa cells inhibited recycling of the prostaglandin D₂ (PGD₂) DP1 receptor (DP1) to the cell surface after agonist-induced internalization and that L-PGDS overexpression had the opposite effect. Depletion of endogenous Rab4 prevented l-PGDS-mediated recycling of DP1, and l-PGDS depletion inhibited Rab4-dependent recycling of DP1, indicating that both proteins are mutually involved in this pathway. DP1 stimulation promoted its interaction through its intracellular C terminus with Rab4, which was increased by l-PGDS. Confocal microscopy revealed that DP1 activation induces l-PGDS/Rab4 co-localization. l-PGDS/Rab4 and DP1/Rab4 co-immunoprecipitation levels were increased by DP1 agonist treatment. Pulldown assays with purified GST-l-PGDS and His₆-Rab4 indicated that both proteins interact directly. l-PGDS interacted preferentially with the inactive, GDP-locked Rab4S22N variant rather than with WT Rab4 or with constitutively active Rab4Q67L proteins. Overexpression and depletion experiments disclosed that l-PGDS partakes in Rab4 activation following DP1 stimulation. Experiments with deletion mutants and synthetic peptides revealed that amino acids 85-92 in l-PGDS are involved in its interaction with Rab4 and in its effect on DP1 recycling. Of note, GTPγS loading and time-resolved FRET assays with purified proteins suggested that l-PGDS enhances GDP-GTP exchange on Rab4. Our results reveal how l-PGDS, which produces the agonist for DP1, regulates DP1 recycling by participating in Rab4 recruitment and activation.

Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3

In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.

GPCR Homology Model Generation for Lead Optimization

The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Stabilised G protein-coupled receptors in structure-based drug design: a case study with adenosine A2A receptor

The first example of structure-based drug design with stabilised GPCRs has enabled the identification of a preclinical candidate for the treatment of Parkinson's disease.

Structure-activity relationships and molecular modeling of 1,2,4-triazoles as adenosine receptor antagonists

The structure-activity relationship (SAR) for a novel class of 1,2,4-triazole antagonists of the human A(2A) adenosine receptor (hA(2A)AR) was explored. Thirty-three analogs of a ligand that was discovered in a structure-based virtual screen against the hA(2A)AR were tested in hA(1), A(2A), and A(3) radioligand binding assays and in functional assays for the A(2B)AR subtype. As a series of closely related analogs of the initial lead, 1, did not display improved binding affinity or selectivity, molecular docking was used to guide the selection of more distantly related molecules. This resulted in the discovery of 32, a hA(2A)AR antagonist (K(i) 200 nM) with high ligand efficiency. In the light of the SAR for the 1,2,4-triazole scaffold, we also investigated the binding mode of these compounds based on docking to several A(2A)AR crystal structures.

Predicting the biological activities through QSAR analysis and docking-based scoring

Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.

Receptor-based QSAR study for a series of 3,3-disubstituted-5-aryl oxindoles and 6-aryl benzimidazol-2-ones derivatives as progesterone receptor inhibitors

Receptor-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 54 progesterone receptor (PR) inhibitors. The established CoMFA model from the training set gives statistically significant results with the cross-validated q (2) of 0.534 and non-cross-validated [Formula: see text] of 0.947. The best CoMSIA model was derived by the combination of steric field and hydrophobic field with a q (2) of 0.615 and [Formula: see text] of 0.954. A test set of 18 compounds was used to validate the predictive ability of the two models. The predicted correlation coefficients [Formula: see text] are 0.681 and 0.677 for CoMFA and CoMSIA models, respectively. Based on the CoMFA maps, the key structural characters of progesterone receptor inhibitors are identified. Moreover, the binding modes of oxindoles and benzimidazol-2-ones are also given by the quantum mechanical/molecular mechanical (QM/MM) calculations. This may provide useful information for drug design.

The use of G-protein coupled receptor models in lead optimization

With the emerging new crystal structures of G-protein coupled receptors (GPCRs), the number of reported in silico receptor models vastly increases every year. The use of these models in lead optimization (LO) is investigated here. Although there are many studies where GPCR models are used to identify new chemotypes by virtual screening, the classical application in LO is rarely reported. The reason for this may be that the quality of a model, which is appropriate for atomistic modeling, must be very high, and the biology of GPCR ligand-dependent signaling is still not fully understood. However, the few reported studies show that GPCR models can be used efficiently in LO for various problems, such as affinity optimization or tuning of physicochemical parameters.

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor

In this study, we evaluated the applicability of ligand-based and structure-based models to quantitative affinity predictions and virtual screenings for ligands of the beta(2)-adrenergic receptor, a G protein-coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand-based consensus model (LI-CM) seems to be the best choice, while the structure-based MM-GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure-based MM-GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded.

Comparative docking study of anibamine as the first natural product CCR5 antagonist in CCR5 homology models

Anibamine, a novel pyridine quaternary alkaloid recently isolated from Aniba sp., has been found to effectively bind to the chemokine receptor CCR5 with an IC(50) at 1 microM in competition with (125)I-gp120, an HIV viral envelope protein binding to CCR5 with high affinity. Since CCR5, a G-protein-coupled receptor, is an essential coreceptor for the human immunodeficiency virus type I (HIV-1) entry to host cells, a CCR5 antagonist that inhibits the cellular entry of HIV-1 provides a new therapy choice for the treatment of HIV. Anibamine provides a novel structural skeleton that is remarkably different from all lead compounds previously identified as CCR5 antagonists. Here, we report comparative docking studies of anibamine with several other known CCR5 antagonists in two CCR5 homology models built based on the crystal structures of bovine rhodopsin and human beta(2)-adrenergic receptor. The binding pocket of anibamine has some common features shared with other high affinity CCR5 antagonists, suggesting that they may bind in similar binding sites and/or modes. At the same time, several unique binding features of anibamine were identified, and it will likely prove beneficial in future molecular design of novel CCR5 antagonists based on the anibamine scaffold.

Medicinal chemistry of the A3 adenosine receptor: agonists, antagonists, and receptor engineering

A(3) adenosine receptor (A(3)AR) ligands have been modified to optimize their interaction with the A(3)AR. Most of these modifications have been made to the N(6) and C2 positions of adenine as well as the ribose moiety, and using a combination of these substitutions leads to the most efficacious, selective, and potent ligands. A(3)AR agonists such as IB-MECA and Cl-IB-MECA are now advancing into Phase II clinical trials for treatments targeting diseases such as cancer, arthritis, and psoriasis. Also, a wide number of compounds exerting high potency and selectivity in antagonizing the human (h)A(3)AR have been discovered. These molecules are generally characterized by a notable structural diversity, taking into account that aromatic nitrogen-containing monocyclic (thiazoles and thiadiazoles), bicyclic (isoquinoline, quinozalines, (aza)adenines), tricyclic systems (pyrazoloquinolines, triazoloquinoxalines, pyrazolotriazolopyrimidines, triazolopurines, tricyclic xanthines) and nucleoside derivatives have been identified as potent and selective A(3)AR antagonists. Probably due to the "enigmatic" physiological role of A(3)AR, whose activation may produce opposite effects (for example, concerning tissue protection in inflammatory and cancer cells) and may produce effects that are species dependent, only a few molecules have reached preclinical investigation. Indeed, the most advanced A(3)AR antagonists remain in preclinical testing. Among the antagonists described above, compound OT-7999 is expected to enter clinical trials for the treatment of glaucoma, while several thiazole derivatives are in development as antiallergic, antiasthmatic and/or antiinflammatory drugs.