Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach

Second generation Al18F-labeled D-amino acid peptide for CXCR4 targeted molecular imaging

BACKGROUND

The C-X-C chemokine receptor type 4 (CXCR4) is overexpressed in many cancers, e.g. multiple myeloma and acute leukemia, yet solely [68Ga]PentixaFor is used for clinical PET imaging. The aim of this study was to develop and assess a second generation Al18F-labeled D-amino acid peptide based on the viral macrophage inflammatory protein II for CXCR4 targeted molecular imaging.

METHODS

We designed a library of monomer and multimer constructs and evaluated their binding affinity for human and mouse CXCR4. Based on these results, we selected the best vector molecule for development of an Al18F-labeled ligand, [18F]AlF-NOTA-2xDV1(c11sc12s), which was further evaluated in a cell-based binding assay to assess its binding properties and specificity for CXCR4. Next, pharmacokinetics and tumor uptake of [18F]AlF-NOTA-2xDV1(c11sc12s) were evaluated in naïve mice and mice with xenografts derived from U87.CXCR4 cells. Finally, we performed an imaging study in a non-human primate to assess the in vivo distribution of this novel radioligand in a species closely related to humans.

RESULTS

The lead ligand AlF-NOTA-2xDV1(c11sc12s) showed six-fold higher affinity for human CXCR4 compared to Ga-Pentixafor. The corresponding radiotracer was obtained in a good radiochemical yield of 40.1 ± 13.5 % (n = 4) and apparent molar activity of 20.4 ± 3.3 MBq/nmol (n = 4) after optimization. In U87.CD4.CXCR4 cell binding assays, the total bound fraction of [18F]AlF-NOTA-(2×)DV1(c11sc12s) was 32.4 ± 1.8 %. This fraction could be reduced by 82.5 % in the presence of 75 μM AMD3100. In naïve mice, [18F]AlF-NOTA-2xDV1(c11sc12s) accumulated in organs expressing mouse CXCR4, e.g. the liver (SUVmean (mean standardized uptake value) 75 min p.i. 11.7 ± 0.6), which was blockable by co-injecting AMD3100 (5 mg/kg). In U87.CXCR4 xenografted tumor mice, the tumor uptake of [18F]AlF-NOTA-2xDV1(c11sc12s) remained low (SUVmean 0.5 ± 0.1), but was reduced by co-administration of AMD3100. Surprisingly, [18F]AlF-NOTA-2xDV1(c11sc12s) exhibited a similar biodistribution in a non-human primate as in mice indicating off-target binding of [18F]AlF-NOTA-2xDV1(c11sc12s) in liver tissue. We confirmed that [18F]AlF-NOTA-2xDV1(c11sc12s) is taken up by hepatocytes using in vitro studies and that the uptake can be blocked with AMD3100 and rifampicin, a potent organic anion-transporting-polypeptide (OATP)1B1 and OATP1B3 inhibitor.

CONCLUSION

The second generation D-peptide AlF-NOTA-2xDV1(c11sc12s) showed high affinity for human CXCR4 and the corresponding radiotracer was produced in good radiochemical yields. However, [18F]AlF-NOTA-2xDV1(c11sc12s) is not specific for CXCR4 and is also a substrate for OATP1B1 and/or OATP1B3, known to mediate hepatic uptake. Therefore, D-amino acid peptides, based on the viral macrophage inflammatory protein II, are not the prefered vector molecule for the development of CXCR4 targeting molecular imaging tools.

Preliminary study to identify CXCR4 inhibitors as potential therapeutic agents for Alzheimer's and Parkinson's diseases

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Discovery of HIV entry inhibitors via a hybrid CXCR4 and CCR5 receptor pharmacophore-based virtual screening approach

Chemokine receptors are key regulators of cell migration in terms of immunity and inflammation. Among these, CCR5 and CXCR4 play pivotal roles in cancer metastasis and HIV-1 transmission and infection. They act as essential co-receptors for HIV and furnish a route to the cell entry. In particular, inhibition of either CCR5 or CXCR4 leads very often the virus to shift to a more virulent dual-tropic strain. Therefore, dual receptor inhibition might improve the therapeutic strategies against HIV. In this study, we aimed to discover selective CCR5, CXCR4, and dual CCR5/CXCR4 antagonists using both receptor- and ligand-based computational methods. We employed this approach to fully incorporate the interaction attributes of the binding pocket together with molecular dynamics (MD) simulations and binding free energy calculations. The best hits were evaluated for their anti-HIV-1 activity against CXCR4- and CCR5-specific NL4.3 and BaL strains. Moreover, the Ca²⁺ mobilization assay was used to evaluate their antagonistic activity. From the 27 tested compounds, three were identified as inhibitors: compounds 27 (CCR5), 6 (CXCR4) and 3 (dual) with IC₅₀ values ranging from 10.64 to 64.56 μM. The binding mode analysis suggests that the active compounds form a salt bridge with the glutamates and π-stacking interactions with the aromatic side chains binding site residues of the respective co-receptor. The presented hierarchical virtual screening approach provides essential aspects in identifying potential antagonists in terms of selectivity against a specific co-receptor. The compounds having multiple heterocyclic nitrogen atoms proved to be relatively more specific towards CXCR4 inhibition as compared to CCR5. The identified compounds serve as a starting point for further development of HIV entry inhibitors through synthesis and quantitative structure-activity relationship studies.

An Agonist of the CXCR4 Receptor Strongly Promotes Regeneration of Degenerated Motor Axon Terminals

The activation of the G-protein coupled receptor CXCR4 by its ligand CXCL12α is involved in a large variety of physiological and pathological processes, including the growth of B cells precursors and of motor axons, autoimmune diseases, stem cell migration, inflammation, and several neurodegenerative conditions. Recently, we demonstrated that CXCL12α potently stimulates the functional recovery of damaged neuromuscular junctions via interaction with CXCR4. This result prompted us to test the neuroregeneration activity of small molecules acting as CXCR4 agonists, endowed with better pharmacokinetics with respect to the natural ligand. We focused on NUCC-390, recently shown to activate CXCR4 in a cellular system. We designed a novel and convenient chemical synthesis of NUCC-390, which is reported here. NUCC-390 was tested for its capability to induce the regeneration of motor axon terminals completely degenerated by the presynaptic neurotoxin α-Latrotoxin. NUCC-390 was found to strongly promote the functional recovery of the neuromuscular junction, as assayed by electrophysiology and imaging. This action is CXCR4 dependent, as it is completely prevented by AMD3100, a well-characterized CXCR4 antagonist. These data make NUCC-390 a strong candidate to be tested in human therapy to promote nerve recovery of function after different forms of neurodegeneration.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

Discovery and characterization of novel small-molecule CXCR4 receptor agonists and antagonists

The chemokine CXCL12 (SDF-1) and its cognate receptor CXCR4 are involved in a large number of physiological processes including HIV-1 infectivity, inflammation, tumorigenesis, stem cell migration, and autoimmune diseases. While previous efforts have identified a number of CXCR4 antagonists, there have been no small molecule agonists reported. Herein, we describe the identification of a novel series of CXCR4 modulators, including the first small molecules to display agonist behavior against this receptor, using a combination of structure- and ligand-based virtual screening. These agonists produce robust calcium mobilization in human melanoma cell lines which can be blocked by the CXCR4-selective antagonist AMD3100. We also demonstrate the ability of these new agonists to induce receptor internalization, ERK activation, and chemotaxis, all hallmarks of CXCR4 activation. Our results describe a new series of biologically relevant small molecules that will enable further study of the CXCR4 receptor and may contribute to the development of new therapeutics.

Integrating Pharmacophore into Membrane Molecular Dynamics Simulations to Improve Homology Modeling of G Protein-coupled Receptors with Ligand Selectivity: A2A Adenosine Receptor as an Example

Homology modeling has been applied to fill in the gap in experimental G protein-coupled receptors structure determination. However, achievement of G protein-coupled receptors homology models with ligand selectivity remains challenging due to structural diversity of G protein-coupled receptors. In this work, we propose a novel strategy by integrating pharmacophore and membrane molecular dynamics (MD) simulations to improve homology modeling of G protein-coupled receptors with ligand selectivity. To validate this integrated strategy, the A2A adenosine receptor (A2A AR), whose structures in both active and inactive states have been established, has been chosen as an example. We performed blind predictions of the active-state A2A AR structure based on the inactive-state structure and compared the performance of different refinement strategies. The blind prediction model combined with the integrated strategy identified ligand-receptor interactions and conformational changes of key structural elements related to the activation of A2 A AR, including (i) the movements of intracellular ends of TM3 and TM5/TM6; (ii) the opening of ionic lock; (iii) the movements of binding site residues. The integrated strategy of pharmacophore with molecular dynamics simulations can aid in the optimization in the identification of side chain conformations in receptor models. This strategy can be further investigated in homology modeling and expand its applicability to other G protein-coupled receptor modeling, which should aid in the discovery of more effective and selective G protein-coupled receptor ligands.

Anti-HIV small-molecule binding in the peptide subpocket of the CXCR4:CVX15 crystal structure

The CXC chemokine receptor 4 (CXCR4) is involved in chemotaxis and serves as a coreceptor for T-tropic HIV-1 viral entry, thus making this receptor an attractive drug target. Recently, crystal structures of CXCR4 were reported as complexes with the small molecule IT1t and the CVX15 peptide. Follow-up efforts to model different antagonists into the small molecule CXCR4:IT1t crystal structure did not generate poses consistent with either the X-ray crystal structure or site-directed mutagenesis (SDM). Here, we compare the binding pockets of the two CXCR4 crystal structures, revealing differences in helices IV, V, VI, and VII, with major differences for the His203 residue buried in the binding pocket. The small molecule antagonist AMD11070 was docked into both CXCR4 crystal structures. An AMD11070 pose identified from the CXCR4:CVX15 model presented interactions with Asp171, Glu288, Trp94, and Asp97, consistent with published SDM data, thus suggesting it is the bioactive pose. A CXCR4 receptor model was optimized around this pose of AMD11070, and the resulting model correlated HIV-1 inhibition with MM-GBSA docking scores for a congeneric AMD11070-like series. Subsequent NAMFIS NMR results successfully linked the proposed binding pose to an independent experimental structure. These results strongly suggest that not all small molecules will bind to CXCR4 in a similar manner as IT1t. Instead, the CXCR4:CVX15 crystal structure may provide a binding locus for small organic molecules that is more suitable than the secondary IT1t site. This work is expected to provide modeling insights useful for future CXCR4 antagonist and X4-tropic HIV-1 based drug design efforts.

Homology modeling of human muscarinic acetylcholine receptors

We have developed homology models of the acetylcholine muscarinic receptors M₁R-M₅R, based on the β₂-adrenergic receptor crystal as the template. This is the first report of homology modeling of all five subtypes of acetylcholine muscarinic receptors with binding sites optimized for ligand binding. The models were evaluated for their ability to discriminate between muscarinic antagonists and decoy compounds using virtual screening using enrichment factors, area under the ROC curve (AUC), and an early enrichment measure, LogAUC. The models produce rational binding modes of docked ligands as well as good enrichment capacity when tested against property-matched decoy libraries, which demonstrates their unbiased predictive ability. To test the relative effects of homology model template selection and the binding site optimization procedure, we generated and evaluated a naïve M₂R model, using the M₃R crystal structure as a template. Our results confirm previous findings that binding site optimization using ligand(s) active at a particular receptor, i.e. including functional knowledge into the model building process, has a more pronounced effect on model quality than target-template sequence similarity. The optimized M₁R-M₅R homology models are made available as part of the Supporting Information to allow researchers to use these structures, compare them to their own results, and thus advance the development of better modeling approaches.

Discovery and computer aided potency optimization of a novel class of small molecule CXCR4 antagonists

Amongst the chemokine signalling axes involved in cancer, chemokine CXCL12 acting on chemokine receptor CXCR4 is particularly significant since it orchestrates migration of cancer cells in a tissue-specific metastatic process. High CXCR4 tumour expression is associated with poor prognosis of lung, brain, CNS, blood and breast cancers. We have identified a new class of small molecule CXCR4 antagonists based on the use of computational modelling studies in concert with experimental determination of in vitro activity against CXCL12-induced intracellular calcium mobilisation, proliferation and chemotaxis. Molecular modelling proved to be a useful tool in rationalising our observed potencies, as well as informing the direction of the synthetic efforts aimed at producing more potent compounds.

ALiBERO: evolving a team of complementary pocket conformations rather than a single leader

Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC = 82.2 and ROC((2%)) = 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways.

AREAS COVERED

The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques.

EXPERT OPINION

The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.